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Landslides are one of the most frequent and devastating natural disasters around the world with intensifying impacts on human lives and the environment. To effectively deal with landslides and their consequences, it is primarily important to demarcate areas susceptible to landslides. This can be done through landslide susceptibility mapping (LSM). In this study, a novel approach for landslide susceptibility mapping based on the uncertain and parameter-free density-based clustering (UPFDBSCAN) algorithm was proposed. It merges the ideas from the dominant set clustering algorithm, the DBSCAN algorithm, and the uncertain data modeling method. The study aims to overcome the limitations of depending on user-defined density parameters, the inability to identify clusters of varied densities, and to model the uncertain data, in the DBSCAN algorithm and most of the existing clustering algorithms. This improves the clustering accuracy and efficiency for LSM modeling. For this purpose, the proposed model was experimented with an inventory containing 506 samples of landslide and non-landslide locations, and data of 7 landslide influencing factors from the Baota District in Shaanxi, China. The model's performance was evaluated and compared with existing clustering-based LSM models as state-of-the-art methods based on standard evaluation metrics. The results revealed that the proposed model obtained the highest performance (sensitivity = 0.935, specificity = 0.944, accuracy = 0.939, AUC = 0.881, JC = 0.898, and purity = 0.899) and it was thus superior to the other models. This study's findings can help decision-makers, policymakers, and land stakeholders to implement significant strategies with early warning systems to predict, prevent, and mitigate the occurrence and impacts of landslides. [ABSTRACT FROM AUTHOR]

Landslide susceptibility assessment plays a vital role in understanding landslide information in advance and taking preventive as well as control measures. The need to initially specify the number of clusters, difficulty in handling noise and quantifying rainfall data, limits the application of traditional clustering models in landslide susceptibility assessment which then lowers their performance accuracy. Thus, to overcome these limitations, this study proposed an improved clustering algorithm titled the DBSCAN-AHD algorithm, which combines the traditional DBSCAN (Density Based Spatial Clustering of Application with Noise) algorithm and an Adaptive Hausdorff Distance (AHD) for landslide susceptibility modeling. Firstly, AHD was introduced to the traditional DBSCAN to quantify rainfall. Then, the DBSCAN-AHD grouped the mapping units with similar topology and geology characteristics into subclasses without specifying the number of clusters in advance for its ability to handle noise in the data. Furthermore, the LD-EV (landslide density or eigenvalues) approach was introduced to obtain the susceptibility levels using K-means algorithm. Finally, to verify the model's performance, statistical indices and the area under the curve (AUC) were applied and compared to the traditional DBSCAN, KPSO, K-means and Hierarchical Clustering algorithms, whereby, the proposed model outperformed the others. Also, the obtained susceptibility map can provide references in taking relevant preventive and control measures. [ABSTRACT FROM AUTHOR]

Uncertainty about data appears in many real-world applications and an important issue is how to manage, analyze and solve optimization problems over such data. An important tool for data analysis is clustering. When the data set is uncertain, we can model them as a set of probabilistic points each formalized as a probability distribution function which describes the possible locations of the points. In this paper, we study k-center problem for probabilistic points in a general metric space. First, we present a fast greedy approximation algorithm that builds k centers using a farthest-first traversal in k iterations. This algorithm improves the previous approximation factor of the unrestricted assigned k-center problem from 10 (see [1]) to 6. Next, we restrict the centers to be selected from all the probabilistic locations of the given points and we show that an optimal solution for this restricted setting is a 2-approximation factor solution for an optimal solution of the assigned k-center problem with expected distance assignment. Using this idea, we improve the approximation factor of the unrestricted assigned k-center problem to 4 by increasing the running time. The algorithm also runs in polynomial time when k is a constant. Additionally, we implement our algorithms on three real data sets. The experimental results show that in practice the approximation factors of our algorithms are better than in theory for these data sets. Also we compare the results of our algorithm with the previous works and discuss about the achieved results. At the end, we present our theoretical results for probabilistic k-median clustering. [ABSTRACT FROM AUTHOR]

Most data mining algorithms are designed for traditional type of data objects which are referred to as certain data objects. Certain data objects contain no uncertainty information and are represented by a single point. Capturing uncertainty can result in better performance of algorithms as they might generate more accurate results. There are different ways of modeling uncertainty for data objects, two of the most popular ones are: (1) considering a group of points for each object and (2) considering a probability density function (pdf) for each object. Objects modeled in these ways are referred to as uncertain data objects. Fuzzy clustering is a well-established field of research for certain data. When fuzzy clustering algorithms are used, degrees of membership are generated for assignment of objects to clusters which gives the flexibility to express that objects can belong to more than one cluster. To the best of our knowledge, for uncertain data, there is only one existing fuzzy clustering algorithm in the literature. The existing uncertain fuzzy clustering algorithm, however, cannot properly create non-convex shaped clusters, and therefore, its performance is not that well on uncertain data sets with arbitrary-shaped clusters—clusters that are non-convex, unconventional, and possibly nonlinearly separable. In this paper, we propose a novel fuzzy kernel K-medoids clustering algorithm for uncertain objects which works well on data sets with arbitrary-shaped clusters. We show through several experiments on synthetic and real data that the proposed algorithm outperforms the competitor algorithms: certain fuzzy K-medoids and the uncertain fuzzy K-medoids. [ABSTRACT FROM AUTHOR]

Clustering validity indices are the main tools for evaluating the quality of formed clusters and determining the correct number of clusters. They can be applied on the results of clustering algorithms to validate the performance of those algorithms. In this paper, two clustering validity indices named uncertain Silhouette and Order Statistic, are developed for uncertain data. To the best of our knowledge, there is not any clustering validity index in the literature that is designed for uncertain objects and can be used for validating the performance of uncertain clustering algorithms. Our proposed validity indices use probabilistic distance measures to capture the distance between uncertain objects. They outperform existing validity indices for certain data in validating clusters of uncertain data objects and are robust to outliers. The Order Statistic index in particular, a general form of uncertain Dunn validity index (also developed here), is well capable of handling instances where there is a single cluster that is relatively scattered (not compact) compared to other clusters, or there are two clusters that are close (not well-separated) compared to other clusters. The aforementioned instances can potentially result in the failure of existing clustering validity indices in detecting the correct number of clusters. [ABSTRACT FROM AUTHOR]

Enabling information systems to face anomalies in the presence of uncertainty is a compelling and challenging task. In this work the problem of unsupervised outlier detection in large collections of data objects modeled by means of arbitrary multidimensional probability density functions is considered. We present a novel definition of uncertain distance-based outlier under the attribute level uncertainty model, according to which an uncertain object is an object that always exists but its actual value is modeled by a multivariate pdf. According to this definition an uncertain object is declared to be an outlier on the basis of the expected number of its neighbors in the dataset. To the best of our knowledge this is the first work that considers the unsupervised outlier detection problem on data objects modeled by means of arbitrarily shaped multidimensional distribution functions. We present the UDBOD algorithm which efficiently detects the outliers in an input uncertain dataset by taking advantages of three optimized phases, that are parameter estimation, candidate selection, and the candidate filtering. An experimental campaign is presented, including a sensitivity analysis, a study of the effectiveness of the technique, a comparison with related algorithms, also in presence of high dimensional data, and a discussion about the behavior of our technique in real case scenarios. [ABSTRACT FROM AUTHOR]

In recent years, numerous applications have been continuously generating large amounts of uncertain data. The advanced analysis queries such as skyline operators are essential topics to extract interesting objects from the vast uncertain dataset. Recently, the MapReduce system has been widely used in the area of big data analysis. Although the probabilistic skyline query is not decomposable, it does not make sense to implement the probabilistic skyline query in the MapReduce framework. This paper proposes an effective parallel method called parallel computation of probabilistic skyline query (PCPS) that can measure the probabilistic skyline set in one MapReduce computation pass. The proposed method takes into account the critical sections and detects data with a high probability of existence through a proposed smart sampling algorithm. PCPS implements a new approach to the fair allocation of input data. The experimental results indicate that our proposed approach can not only reduce the processing time of the probabilistic skyline queries, but also achieve fair precision with varying dimensionality degrees. [ABSTRACT FROM AUTHOR]

In this article, we consider vector optimization problems with uncertain data. We first formulate optimistic counterparts of the reference problems and propose concepts of efficient solutions to such counterparts. We then introduce concepts of pointwise and global well-posedness for optimistic counterparts. Using the generalized Gerstewitz's function and properties of elements in the image space, we establish the relationships between well-posedness properties for the reference problems and that for scalar optimization ones. Based on such relations, we have studied sufficient conditions of these well-posedness properties for the considered problems via the corresponding scalar problems. Finally, by virtue of a forcing function, the characterizations of the two concepts of well-posedness for such problems are presented. [ABSTRACT FROM AUTHOR]

In the case of current technology, most of the measurements are focused on geometric distance, and the distribution of data is not considered. In order to compensate for this shortcoming of geometric distance measurement, this paper uses the KL distance as the similarity measurement standard for uncertain data, and the DOUD_C algorithm and COUD_C algorithm are proposed respectively in the discrete domain and continuous domain. In order to solve the problem of efficient clustering of the high dimensional data, this paper considers the data structure of grid, and BROUD_C algorithm is proposed. According to the adjacency characteristic of the grid, the cluster process is extended continuously, the algorithm can find clusters of arbitrary shapes, and we can filter a large number of isolated points, it solves the uncertain data clustering problem effectively in the obstacle space. The experimental results show that compared to the OBS_UK_means with VPA and SDA pruning algorithm and FOPTICS algorithm, the clustering performance of BROUD_C algorithm is more significant and CPU has less execution time in the obstacle space. [ABSTRACT FROM AUTHOR]

Uncertain data are data with uncertainty information, which exist widely in database applications. In recent years, uncertainty in data has brought challenges in almost all database management areas such as data modeling, query representation, query processing, and data mining. There is no doubt that uncertain data management has become a hot research topic in the field of data management. In this study, we explore problems in managing uncertain data, present state-of-the-art solutions, and provide future research directions in this area. The discussed uncertain data management techniques include data modeling, query processing, and data mining in uncertain data in the forms of relational, XML, graph, and stream. [ABSTRACT FROM AUTHOR]

Universal generating function (UGF) technology is the basic approach to reliability evaluation of multi-state systems. For products with high reliability and long lifetime in practical engineering, their system structure is usually uncertain and the external working condition is extremely complex. Thus, accurate failure or degradation data of components are difficult to define. Data are often characterized by types of randomness, probability, grey, fuzzy and other uncertain numbers. Based on interval grey number, the concept of generalized uncertain number is proposed. In this paper, we first unify arbitrary type of uncertain numbers, propose the definition of generalized standard uncertain number and study algorithm such as addition, subtraction, multiplication and division between them. Based on generalized standard uncertain number, generalized universal generating function, which is an extension of fuzzy UGF, is proposed. Several commonly used generalized universal generating operators are discussed with system connection structure between components. According to the relationship between output performance and system demand level, reliability indexes such as availability, expected output performance and mean performance deficiency are evaluated. Comparisons in a real case show the effectiveness of the proposed approach. Therefore, relevant results can strengthen the theoretical research depth of uncertain reliability and definitely expand the application scope of fuzzy universal generating function. [ABSTRACT FROM AUTHOR]

In this paper, we consider a variational inequality model of migration in which populations move according to the utility functions corresponding to various locations and movement costs. In contrast to previously studied models based on variational inequalities, we take into account the possibility that some of the data in the model are not deterministic but, instead, are known through their probability distributions. Our theoretical framework is that of random variational inequalities in Lebesgue spaces. [ABSTRACT FROM AUTHOR]

To facilitate ( k)-Nearest Neighbor queries, the concept of Voronoi decomposition is widely used. In this work, we propose solutions to extend the concept of Voronoi-cells to uncertain data. Due to data uncertainty, the location, the shape and the extent of a Voronoi cell are random variables. To facilitate reliable query processing despite the presence of uncertainty, we employ the concept of possible-Voronoi cells and introduce the novel concept of guaranteed-Voronoi cells: The possible-Voronoi cell of an object U consists of all points in space that have a non-zero probability of having U as their nearest-neighbor; and the guaranteed-Voronoi cell, which consists of all points in space which must have U as their nearest-neighbor. Since exact computation of both types of Voronoi cells is computationally hard, we propose approximate solutions. Therefore, we employ hierarchical access methods for both data and object space. Our proposed algorithm descends both index structures simultaneously, constantly trying to prune branches in both trees by employing the concept of spatial domination. To support ( k)-Nearest Neighbor queries having k > 1, this work further pioneers solutions towards the computation of higher-order possible and higher-order guaranteed Voronoi cells, which consist of all points in space which may (respectively must) have U as one of their k-nearest neighbors. For this purpose, we develop three algorithms to explore our index structures and show that the approach that descends both index structures in parallel yields the fastest query processing times. Our experiments show that we are able to approximate uncertain Voronoi cells of any order much more effectively than the state-of-the-art while improving run-time performance. Since our approach is the first to compute guaranteed-Voronoi cells and higher order (possible and guaranteed) Voronoi cells, we extend the existing state-of-the-art solutions to these concepts, in order to allow a fair experimental evaluation. [ABSTRACT FROM AUTHOR]

Range queries and range joins in metric spaces have applications in many areas, including GIS, computational biology, and data integration, where metric uncertain data exist in different forms, resulting from circumstances such as equipment limitations, high-throughput sequencing technologies, and privacy preservation. We represent metric uncertain data by using an object-level model and a bi-level model, respectively. Two novel indexes, the uncertain pivot B $$^{+}$$ -tree (UPB-tree) and the uncertain pivot B $$^{+}$$ -forest (UPB-forest), are proposed in order to support probabilistic range queries and range joins for a wide range of uncertain data types and similarity metrics. Both index structures use a small set of effective pivots chosen based on a newly defined criterion and employ the B $$^{+}$$ -tree(s) as the underlying index. In addition, we present efficient metric probabilistic range query and metric probabilistic range join algorithms, which utilize validation and pruning techniques based on derived probability lower and upper bounds. Extensive experiments with both real and synthetic data sets demonstrate that, compared against existing state-of-the-art indexes for metric uncertain data, the UPB-tree and the UPB-forest incur much lower construction costs, consume less storage space, and can support more efficient metric probabilistic range queries and metric probabilistic range joins. [ABSTRACT FROM AUTHOR]

This paper studies the problem of probabilistic range query over uncertain data. Although existing solutions could support such query, it still has space for improvement. In this paper, we firstly propose a novel index called S-MRST for indexing uncertain data. For one thing, via using an irregular shape for bounding uncertain data, it has a stronger space pruning ability. For another, by taking the gradient of probability density function into consideration, S-MRST is also powerful in terms of probability pruning ability. More important, S-MRST is a general index which could support multiple types of probabilistic queries. Theoretical analysis and extensive experimental results demonstrate the effectiveness and efficiency of the proposed index. [ABSTRACT FROM AUTHOR]

Many existing data mining algorithms search interesting patterns from transactional databases of precise data. However, there are situations in which data are uncertain. Items in each transaction of these probabilistic databases of uncertain data are usually associated with existential probabilities, which express the likelihood of these items to be present in the transaction. When compared with mining from precise data, the search space for mining from uncertain data is much larger due to the presence of the existential probabilities. This problem is worsened as we are moving to the era of Big data. Furthermore, in many real-life applications, users may be interested in a tiny portion of this large search space for Big data mining. Without providing opportunities for users to express the interesting patterns to be mined, many existing data mining algorithms return numerous patterns-out of which only some are interesting. In this article, we propose an algorithm that allows users to express their interest in terms of constraints, uses the MapReduce model to mine uncertain Big data for frequent patterns that satisfy the user-specified anti-monotone and monotone constraints, as well as balance the load. [ABSTRACT FROM AUTHOR]

Uncertain data has been very common in many applications. In this paper, we consider the one-center problem for uncertain data on tree networks. In this problem, we are given a tree T and n (weighted) uncertain points each of which has m possible locations on T associated with probabilities. The goal is to find a point $$x^*$$ on T such that the maximum (weighted) expected distance from $$x^*$$ to all uncertain points is minimized. To the best of our knowledge, this problem has not been studied before. We propose a refined prune-and-search technique that solves the problem in linear time. [ABSTRACT FROM AUTHOR]

Many applications such as location-based services and wireless sensor networks generate and deal with uncertain time series (UTS), where the 'exact' value at each timestamp is unknown. Traditional correlation analysis and search techniques developed for standard time series are inadequate for UTS data analysis required in such applications. Motivated by this need, we propose suitable concepts and techniques for UTS correlation analysis. We formalize the notion of normalization and correlation for UTS in two general settings based on the available information at each timestamp: (1) PDF-based UTS (having probability density function) and (2) multiset-based UTS (having multiset of observed values). For each case, we formulate correlation as a random variable and develop techniques to determine the underlying probability density function. For setup (2), we also present probabilistic pruning and sampling techniques to speed up the search process. We conducted numerous experiments to evaluate the performance of the proposed techniques under different configurations using the UCR benchmark datasets. Our results indicate effectiveness of the proposed techniques. For setup (2), in particular, our results show significant improvement in space utilization and computation time. We believe the proposed ideas and solutions lend themselves to powerful tools for UTS analysis and search tasks. [ABSTRACT FROM AUTHOR]

Various large-scale data have been generated in a variety of application fields, since the Internet began to be widely used. Accordingly, researchers have developed various data mining methods for pervasive human-centric computing to deal with the data and discover interesting knowledge. Frequent pattern mining is one of the main issues in data mining, which finds meaningful pattern information from databases. In this area, not only precise data but also uncertain data can be generated depending on environments of data generation. Since the concept of uncertain frequent pattern mining was proposed to overcome the limitations of traditional approaches that cannot deal with uncertain data with existential probabilities of items, several relevant methods have been developed. In this paper, we introduce and analyze state-of-the-art methods based on tree structures, and propose a new uncertain frequent pattern mining approach. We also compare algorithm performance and discuss characteristics of them. [ABSTRACT FROM AUTHOR]

In association rule mining, the trade-off between avoiding harmful spurious rules and preserving authentic ones is an ever critical barrier to obtaining reliable and useful results. The statistically sound technique for evaluating statistical significance of association rules is superior in preventing spurious rules, yet can also cause severe loss of true rules in presence of data error. This study presents a new and improved method for statistical test on association rules with uncertain erroneous data. An original mathematical model was established to describe data error propagation through computational procedures of the statistical test. Based on the error model, a scheme combining analytic and simulative processes was designed to correct the statistical test for distortions caused by data error. Experiments on both synthetic and real-world data show that the method significantly recovers the loss in true rules (reduces type-2 error) due to data error occurring in original statistically sound method. Meanwhile, the new method maintains effective control over the familywise error rate, which is the distinctive advantage of the original statistically sound technique. Furthermore, the method is robust against inaccurate data error probability information and situations not fulfilling the commonly accepted assumption on independent error probabilities of different data items. The method is particularly effective for rules which were most practically meaningful yet sensitive to data error. The method proves promising in enhancing values of association rule mining results and helping users make correct decisions. [ABSTRACT FROM AUTHOR]

The paper is concerned with analysis of elliptic boundary value problems, where the operator is not completely known. Instead, some mean operator and admissible magnitude of perturbations is known. It is assumed that the perturbed operator remains symmetric. These 'equally possible' perturbed operators generate a set of respective solutions. In this paper, two-sided bound for the radius of the solution set are deduced using the duality theory. The bounds are explicitly computable. They show the accuracy limit dictated by the incompletely known data and can be used in cooperation with various numerical methods in order to obtain a reasonable stopping criteria for adaptive methods. [ABSTRACT FROM AUTHOR]

The classifications of uncertain data turned into one of the dreary procedures in the data mining domain. The uncertain data have tuples with distinctive probability distribution, which helps to find similar class of tuples. When we consider an uncertain data, the feature vector will not be a single valued but a function. In this paper, we proposed fuzzy entropy and similarity measure to characterize the uncertain data through binary decision tree algorithm. Fuzzy entropy is used to find the best split point for the decision tree to handle the uncertain data. Similarity measure is used to make the better decision for the uncertain data with high accuracy. Initially, fuzzy entropy for each feature vector is calculated to select the best feature vector. Then, best split is selected from the selected feature vector. With the help of trained uncertain data, the binary tree starts to grow. Once the split point is selected, then the constructed decision tree is evaluated by the testing phase of uncertain data. The testing data are subjected to the trained decision tree to obtain the classified data. The experimental analyses are made to evaluate the performance of the proposed FUDT approach. Proposed FUDT algorithm is compared with the existing classification algorithm UDT in terms of accuracy and running time. The experimental analysis finalizes that our FUDT algorithm outperforms the existing UDT algorithm. [ABSTRACT FROM AUTHOR]

Many previous studies have focused on the extraction of association rules from transaction data. Unfortunately, customer purchasing intentions tend to be uncertain during the decision making process. That is, they cannot be obtained from business transaction data. Therefore, the research problem is how to discover frequent itemsets from uncertain data. This study first proposes a new model to represent consumer uncertainty during the decision making process. This representation scheme is based on possibility distributions. The possibility theory provides an excellent framework for handling uncertain data. In addition, an algorithm is developed to mine plausible-frequent itemsets from uncertain data, which are represented by possibility distributions, and then discover plausible association rules based on these plausible-frequent itemsets. Experimental results show that the proposed model can discover interesting plausible-frequent patterns from survey data which represent customer purchasing decisions. [ABSTRACT FROM AUTHOR]

Uncertain data has already widely existed in many practical applications, such as sensor networks, RFID networks, location-based services and mobile object management, etc. The skyline queries over uncertain data as an important aspect of uncertain data management, has received extensive attention from the database research community currently, due to its importance in many application including multi-criteria decision making, preference answering, market analysis, etc. However, in most uncertainty applications, the uncertain data are usually collected from vast number of independent data sources among geographically scattered sites, which makes the central assembly of data at one location for storage and query is infeasible and inefficient. Taking account of the network delay and limited bandwidth associated with sharing and communicating large amounts of distributed data over an internet, an important and challenging problem in the scenario is to retrieve all the global skyline tuples from all the distributed local sites with minimum communication cost. In this paper, we propose GFS, which is an efficient scheme for probabilistic skyline over distributed uncertain data. GFS firstly prunes the unqualified tuples with the global grid information and further iteratively prune the unqualified tuples with an improved feedback mechanism. Extensive experiments confirm that the effectiveness and the efficiency of the GFS scheme. [ABSTRACT FROM AUTHOR]

Outlier detection, also known as anomaly detection, is a common data mining task in identifying data points that are outside expected patterns in a given dataset. It has useful applications such as network intrusion, system faults, and fraudulent activity. In addition, real world data are uncertain in nature and they may be represented as uncertain data. In this paper, we propose an improved parallel algorithm for outlier detection on uncertain data using density sampling and develop an implementation running on both GPUs and multi-core CPUs, using the OpenCL framework. Our main focus is on GPUs, as they are a cost effective massively parallel floating point processor that is suitable for many data mining applications. Our implementation exploits some key features in GPUs, and is significantly different from a traditional CPU implementation. We first present an improved uncertain outlier detection algorithm. Then, we demonstrate two parallel micro-clustering implementations. The performance and detection quality comparisons demonstrate the benefits of the improved algorithm and parallel implementation on GPUs. [ABSTRACT FROM AUTHOR]

Query processing over uncertain data is very important in many applications due to the existence of uncertainty in real-world data. In this paper, we first elaborate a new and important query in the context of an uncertain database, namely uncertain top-( k, l) range (UTR) query, which retrieves $$l$$ uncertain tuples that are expected to meet score range constraint [ $$CR_1$$ , $$CR_2$$ ] and have the maximum top- k probabilities but no less than a user-specified probability threshold $$q$$ . In order to enable the UTR query answer faster, we put forward some effective pruning rules to reduce the UTR query space, which are integrated into an efficient UTR query procedure. What's more, to improve the efficiency and effectiveness of the UTR query, a parallel UTR (PUTR) query procedure is presented. Extensive experiments have verified the efficiency and effectiveness of our proposed algorithms. It is worth to notice that, comparing to the UTR query procedure, the PUTR query procedure performs much more efficiently and effectively. [ABSTRACT FROM AUTHOR]

One-class classification aims at constructing a distinctive classifier based on one class of examples. Most of the existing one-class classification methods are proposed based on the assumptions that: (1) there are a large number of training examples available for learning the classifier; (2) the training examples can be explicitly collected and hence do not contain any uncertain information. However, in real-world applications, these assumptions are not always satisfied. In this paper, we propose a novel approach called uncertain one-class transfer learning with support vector machine (UOCT-SVM), which is capable of constructing an accurate classifier on the target task by transferring knowledge from multiple source tasks whose data may contain uncertain information. In UOCT-SVM, the optimization function is formulated to deal with uncertain data and transfer learning based on one-class SVM. Then, an iterative framework is proposed to solve the optimization function. Extensive experiments have showed that UOCT-SVM can mitigate the effect of uncertain data on the decision boundary and transfer knowledge from source tasks to help build an accurate classifier on the target task, compared with state-of-the-art one-class classification methods. [ABSTRACT FROM AUTHOR]

Recently, with the growing popularity of Internet of Things (IoT) and pervasive computing, a large amount of uncertain data, e.g., RFID data, sensor data, real-time video data, has been collected. As one of the most fundamental issues of uncertain data mining, uncertain frequent pattern mining has attracted much attention in database and data mining communities. Although there have been some solutions for uncertain frequent pattern mining, most of them assume that the data is independent, which is not true in most real-world scenarios. Therefore, current methods that are based on the independent assumption may generate inaccurate results for correlated uncertain data. In this paper, we focus on the problem of mining frequent itemsets over correlated uncertain data, where correlation can exist in any pair of uncertain data objects (transactions). We propose a novel probabilistic model, called Correlated Frequent Probability model (CFP model) to represent the probability distribution of support in a given correlated uncertain dataset. Based on the distribution of support derived from the CFP model, we observe that some probabilistic frequent itemsets are only frequent in several transactions with high positive correlation. In particular, the itemsets, which are global probabilistic frequent, have more significance in eliminating the influence of the existing noise and correlation in data. In order to reduce redundant frequent itemsets, we further propose a new type of patterns, called global probabilistic frequent itemsets, to identify itemsets that are always frequent in each group of transactions if the whole correlated uncertain database is divided into disjoint groups based on their correlation. To speed up the mining process, we also design a dynamic programming solution, as well as two pruning and bounding techniques. Extensive experiments on both real and synthetic datasets verify the effectiveness and efficiency of the proposed model and algorithms. [ABSTRACT FROM AUTHOR]

Skyline query processing over uncertain data streams has attracted considerable attention in database community recently, due to its importance in helping users make intelligent decisions over complex data in many real applications. Although lots of recent efforts have been conducted to the skyline computation over data streams in a centralized environment typically with one processor, they cannot be well adapted to the skyline queries over complex uncertain streaming data, due to the computational complexity of the query and the limited processing capability. Furthermore, none of the existing studies on parallel skyline computation can effectively address the skyline query problem over uncertain data streams, as they are all developed to address the problem of parallel skyline queries over static certain data sets. In this paper, we formally define the parallel query problem over uncertain data streams with the sliding window streaming model. Particularly, for the first time, we propose an effective framework, named distributed parallel framework to address the problem based on the sliding window partitioning. Furthermore, we propose an efficient approach ( parallel streaming skyline) to further optimize the parallel skyline computation with an optimized streaming item mapping strategy and the grid index. Extensive experiments with real deployment over synthetic and real data are conducted to demonstrate the effectiveness and efficiency of the proposed techniques. [ABSTRACT FROM AUTHOR]

The well-known Shortest Path problem (SP) consists in finding a shortest path from a source to a destination such that the total cost is minimized. The SP models practical and theoretical problems. However, several shortest path applications rely on uncertain data. The Robust Shortest Path problem (RSP) is a generalization of SP. In the former, the cost of each arc is defined by an interval of possible values for the arc cost. The objective is to minimize the maximum relative regret of the path from the source to the destination. This problem is known as the minmax relative regret RSP and it is NP-Hard. We propose a mixed integer linear programming formulation for this problem. The CPLEX branch-and-bound algorithm based on this formulation is able to find optimal solutions for all instances with 100 nodes, and has an average gap of 17 % on the instances with up to 1,500 nodes. We also develop heuristics with emphasis on providing efficient and scalable methods for solving large instances for the minmax relative regret RSP, based on Pilot method and random-key genetic algorithms. To the best of our knowledge, this is the first work to propose a linear formulation, an exact algorithm and metaheuristics for the minmax relative regret RSP. [ABSTRACT FROM AUTHOR]

Uncertain data management, querying and mining have become important because the majority of real world data is accompanied with uncertainty these days. Uncertainty in data is often caused by the deficiency in underlying data collecting equipments or sometimes manually introduced to preserve data privacy. This work discusses the problem of distance-based outlier detection on uncertain datasets of Gaussian distribution. The Naive approach of distance-based outlier on uncertain data is usually infeasible due to expensive distance function. Therefore a cell-based approach is proposed in this work to quickly identify the outliers. The infinite nature of Gaussian distribution prevents to devise effective pruning techniques. Therefore an approximate approach using bounded Gaussian distribution is also proposed. Approximating Gaussian distribution by bounded Gaussian distribution enables an approximate but more efficient cell-based outlier detection approach. An extensive empirical study on synthetic and real datasets show that our proposed approaches are effective, efficient and scalable. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to present a methodology for analyzing the system performance of an industrial system by utilizing uncertain data. Although there have been tremendous advances in the art and science of system evaluation, yet it is very difficult to assess their performance with a very high accuracy or precision. For handling of these uncertainties, fuzzy set theory has been used in the analysis while their corresponding membership functions are generated by solving a nonlinear optimization problem with particle swarm optimization. For finding the critical component of the system which affects the system performance mostly, a composite measure of reliability, availability and maintainability (RAM) named as the RAM-index has been introduced which influences the effects of failure and repair rate parameters on its performance. A time varying failure and repair rate parameters are used in the analysis instead of constant rate models. Finally, the computed results are finally compared with existing methodologies. The suggested framework has been illustrated with the help of a case. [ABSTRACT FROM AUTHOR]

Uncertain data are common due to the increasing usage of sensors, radio frequency identification (RFID), GPS and similar devices for data collection. The causes of uncertainty include limitations of measurements, inclusion of noise, inconsistent supply voltage and delay or loss of data in transfer. In order to manage, query or mine such data, data uncertainty needs to be considered. Hence, this paper studies the problem of top- k distance-based outlier detection from uncertain data objects. In this work, an uncertain object is modelled by a probability density function of a Gaussian distribution. The naive approach of distance-based outlier detection makes use of nested loop. This approach is very costly due to the expensive distance function between two uncertain objects. Therefore, a populated-cells list (PC-list) approach of outlier detection is proposed. Using the PC-list, the proposed top- k outlier detection algorithm needs to consider only a fraction of dataset objects and hence quickly identifies candidate objects for top- k outliers. Two approximate top- k outlier detection algorithms are presented to further increase the efficiency of the top- k outlier detection algorithm. An extensive empirical study on synthetic and real datasets is also presented to prove the accuracy, efficiency and scalability of the proposed algorithms. [ABSTRACT FROM AUTHOR]

Uncertain data management and mining is becoming a hot topic in recent years. However, little attention has been paid to uncertain feature selection so far. In this paper, we introduce the sparse group least absolution shrinkage and selection operator (LASSO) technique to construct a feature selection algorithm for uncertain data. Each uncertain feature is represented with a probability density function. We take each feature as a group of values. Through analysis of the current four sparse feature selection methods, LASSO, elastic net, group LASSO and sparse group LASSO, the sparse group LASSO is introduced to select feature selection from uncertain data. The proposed algorithm can select not only the features between groups, but also the sub-features in groups. As the trained weights of feature groups are sparse, the groups of features with weight zero are removed. Experiments on nine UCI datasets show that feature selection for uncertain data can reduce the number of features and sub-features at the same time. Moreover it can produce comparable accuracy with all features. [ABSTRACT FROM AUTHOR]

Uncertain data have already widely existed in many practical applications recently, such as sensor networks, RFID networks, location-based services, and mobile object management. Query processing over uncertain data as an important aspect of uncertain data management has received increasing attention in the field of database. Uncertain query processing poses inherent challenges and demands non-traditional techniques, due to the data uncertainty. This paper surveys this interesting and still evolving research area in current database community, so that readers can easily obtain an overview of the state-of-the-art techniques. We first provide an overview of data uncertainty, including uncertainty types, probability representation models, and sources of probabilities. We next outline the current major types of uncertain queries and summarize the main features of uncertain queries. Particularly, we present and analyze several typical uncertain queries in detail, such as skyline queries, top- $$k$$ queries, nearest-neighbor queries, aggregate queries, join queries, range queries, and threshold queries over uncertain data. Finally, we present many interesting research topics on uncertain queries that have not yet been explored. [ABSTRACT FROM AUTHOR]

Interval arithmetic can be a useful tool in soft computing. However, working with intervals requires specialized algorithms and appropriate data structures. In this paper, we give an overview of the C++ library C-XSC, which provides many useful data types and functions for (verified) scientific computing, with a special focus on interval arithmetic. We describe its basic features and focus especially on some recent new features that significantly broaden the range of uses of C-XSC, such as dot products in K-fold double precision, sparse data types and BLAS support. In a second section, we describe an application of C-XSC in soft computing in the form of an interface between C-XSC and MATLAB for the DSI-Toolbox, which combines Dempster-Shafer theory to model uncertain data with verified interval arithmetic. For some applications, utilizing C-XSC in MATLAB (or as a standalone) can be more effective than the widely used INTLAB extension for MATLAB, due to the lack of interpretation overhead. As an example, we use C-XSC for the normalization function in the DSI-Toolbox and compare the results and performance to those provided by INTLAB. Using C-XSC in MATLAB also extends the functionality of MATLAB/INTLAB. As an example, the interval error function of C-XSC (this function is not provided by INTLAB) is utilized via a MEX-interface for the verified sampling of the cumulative normal distribution. [ABSTRACT FROM AUTHOR]

The Voronoi diagram is an important technique for answering nearest-neighbor queries for spatial databases. We study how the Voronoi diagram can be used for uncertain spatial data, which are inherent in scientific and business applications. Specifically, we propose the Uncertain-Voronoi diagram (or UV-diagram), which divides the data space into disjoint 'UV-partitions'. Each UV-partition $$P$$ is associated with a set $$S$$ of objects, such that any point $$q$$ located in $$P$$ has the set $$S$$ as its nearest neighbor with nonzero probabilities. The UV-diagram enables queries that return objects with nonzero chances of being the nearest neighbor (NN) of a given point $$q$$. It supports 'continuous nearest-neighbor search', which refreshes the set of NN objects of $$q$$, as the position of $$q$$ changes. It also allows the analysis of nearest-neighbor information, for example, to find out the number of objects that are the nearest neighbors of any point in a given area. A UV-diagram requires exponential construction and storage costs. To tackle these problems, we devise an alternative representation of a UV-diagram, by using a set of UV-cells. A UV-cell of an object $$o$$ is the extent $$e$$ for which $$o$$ can be the nearest neighbor of any point $$q \in e$$. We study how to speed up the derivation of UV-cells by considering its nearby objects. We also use the UV-cells to design the UV-index, which supports different queries, and can be constructed in polynomial time. We have performed extensive experiments on both real and synthetic data to validate the efficiency of our approaches. [ABSTRACT FROM AUTHOR]

As all databases, the bibliometric ones (e.g. Scopus, Web of Knowledge and Google Scholar) are not exempt from errors, such as missing or wrong records, which may obviously affect publication/citation statistics and-more in general-the resulting bibliometric indicators. This paper tries to answer to the question 'What is the effect of database uncertainty on the evaluation of the h-index?', breaking the paradigm of deterministic database analysis and treating responses to database queries as random variables. Precisely an informetric model of the h-index is used to quantify the variability of this indicator with respect to the variability stemming from errors in database records. Some preliminary results are presented and discussed. [ABSTRACT FROM AUTHOR]

Although top- k queries over uncertain data in centralized databases have been studied widely in recent years, it is still a challenging issue in distributed environments. In distributed environments, such as Peer-to-Peer (P2P) systems and sensor networks, there exists an inherent uncertainty on the data objects due to imprecise measurements and network delays. Therefore, it is necessary to study the problem of how to efficiently retrieve top- k uncertain data objects over distributed environments with minimum network overhead. In this paper, we propose a novel approach of processing uncertain top- k queries in large-scale P2P networks, where datasets are horizontally partitioned over peers. In our approach, each peer constructs an Uncertain Quad-Tree (UQ-Tree) index for its local uncertain data, while the P2P network constructs a global index by summarizing the local indexes. Based on the global index, we propose a spatial-pruning algorithm to reduce communication costs and a distributed-pruning algorithm to reduce computation costs. Extensive experiments are conducted to verify the effectiveness and efficiency of the proposed methods in terms of communication costs and response time. [ABSTRACT FROM AUTHOR]

K-nearest neighbours algorithms are among the most popular existing classification methods, due to their simplicity and good performances. Over the years, several extensions of the initial method have been proposed. In this paper, we propose a K-nearest neighbours approach that uses the theory of imprecise probabilities, and more specifically lower previsions. We show that the proposed approach has several assets: it can handle uncertain data in a very generic way, and decision rules developed within this theory allow us to deal with conflicting information between neighbours or with the absence of close neighbour to the instance to classify. We show that results of the basic k-NN and weighted k-NN methods can be retrieved by the proposed approach. We end with some experiments on the classical data sets. [ABSTRACT FROM AUTHOR]