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Mining frequent weighted itemsets (FWIs) from weighted-item transaction databases has recently received research interest. In real-world applications, sparse weighted-item transaction databases (SWITDs) are common. For example, supermarkets have many items, but each transaction has a small number of items. In this paper, we propose an interval word segment (IWS) structure to store and process tidsets for enhancing the effectiveness of mining FWIs from SWITDs. The IWS structure allows the intersection of tidsets between two itemsets to be performed very fast. A map array is proposed for storing a 1-bit index for words. From the map array, 1-bits are mapped to create the tidset of an itemset for faster calculation of the weighted support of itemsets. Experimental results for a number of SWITDs show that the method based on IWS structure outperforms existing methods. [ABSTRACT FROM AUTHOR]

In this study, we explore the combination of two well defined topics in fuzzy systems research: fuzzy rule based systems, and information granulation. Rule based systems are a powerful and well-studied form of knowledge representation, due to their approximation abilities and interpretability. In recent years, these types of systems have become increasingly powerful with regards to modeling accuracy; however, many of these improvements come at the cost of model interpretability. This recent direction of research has left an unexplored avenue towards the generation of increasingly interpretable fuzzy rule based models, which we intend to explore. Information granulation is a relatively new, yet very promising area of research in human centric systems. As a form of knowledge representation, information granulation is very well suited to fuzzy rule based systems, where rules represent linguistic quantities in a, intuitively understandable format. It is notable that the combination of these two concepts has been left largely unstudied. We aim to explore this union by defining a methodology for the construction of a partially granular fuzzy rule based model. The aim of this novel model format is to provide a first step in the improvement of fuzzy model interpretability, through the use of information granulation. We are additionally interested in studying new ways of generating fuzzy rules; hence, we will also look at the use of hierarchical clustering as a potential alternative to the tried and tested Fuzzy C Means clustering algorithm. The models created using hierarchical clustering are then compared with those generated using Fuzzy C Means to evaluate the effectiveness of this algorithm. As a result of these experiments, we demonstrate that partially granular fuzzy rules are capable of providing a significant improvement to fuzzy rule interpretability, and we believe that granular fuzzy models present an exciting avenue of future research in human centric systems. [ABSTRACT FROM AUTHOR]

In order to efficiently use the intrinsic data information, in this study a Discriminative Sparse Subspace Learning (DSSL) model has been investigated for unsupervised feature selection. First, the feature selection problem is formulated as a subspace learning problem. In order to efficiently learn the discriminative subspace, we investigate the discriminative information in the subspace learning process. Second, a two-step TDSSL algorithm and a joint modeling JDSSL algorithm are developed to incorporate the clusters׳ assignment as the discriminative information. Then, a convergence analysis of these two algorithms is provided. A kernelized discriminative sparse subspace learning (KDSSL) method is proposed to handle the nonlinear subspace learning problem. Finally, extensive experiments are conducted on real-world datasets to show the superiority of the proposed approaches over several state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

Abstract: In real-world problems we encounter situations where patterns are described by blocks (families) of features where each of these groups comes with a well-expressed semantics. For instance, in spatiotemporal data we are dealing with spatial coordinates of the objects (say, x–y coordinates) while the temporal part of the objects forms another collection of features. It is apparent that when clustering objects being described by families of features, it becomes intuitively justifiable to anticipate their different role and contribution to the clustering process of the data whereas the clustering is sought to be reflective of an overall structure in the data set. To address this issue, we introduce an agreement based fuzzy clustering—a fuzzy clustering with blocks of features. The detailed investigations are carried out for the well-known algorithm of fuzzy clustering that is fuzzy C-means (FCM). We propose an extended version of the FCM where a composite distance function is endowed with adjustable weights (parameters) quantifying an impact coming from the blocks of features. A global evaluation criterion is used to assess the quality of the obtained results. It is treated as a fitness function in the optimization of the weights through the use of particle swarm optimization (PSO). The behavior of the proposed method is investigated in application to synthetic and real-world data as well as a certain case study. [Copyright &y& Elsevier]

Abstract: In this study, we develop a central profile-based 3D face pose estimation algorithm. The central profile is a unique curve on a 3D face surface that starts from forehead center, goes down through nose ridge, nose tip, mouth center, and ends at a chin tip. The points on the central profile are co-planar and belong to a symmetry plane that separates human face into two identical parts. The central profile is protrusive and has a certain length. Most importantly, the normal vectors of the central profile points are parallel to the symmetry plane. Based on the properties of the central profile, Hough transform is employed to determine the symmetry plane by invoking a voting procedure. An objective function is introduced in the parameter space to quantify the vote importance for face profile points and map the central profile to an accumulator cell with the maximal value. Subsequently, a nose model matching algorithm is used to detect nose tip on the central profile. A pitch angle estimation algorithm is also proposed. The pose estimation experiments completed for a synthetic 3D face model and the FRGC v2.0 3D database demonstrate the effectiveness of the proposed pose estimation algorithm. The obtained central profile detection rate is 99.9%, and the nose tip detection rate has reached 98.16% with error not larger than 10mm. [Copyright &y& Elsevier]

Fuzzy cognitive maps (FCMs) are convenient and widely used architectures for modeling dynamic systems, which are characterized by a great deal of flexibility and adaptability. Several recent works in this area concern strategies for the development of FCMs. Although a few fully automated algorithms to learn these models from data have been introduced, the resulting FCMs are structurally considerably different than those developed by human experts. In particular, maps that were learned from data are much denser (with the density over 90% versus about 40% density of maps developed by humans). The sparseness of the maps is associated with their interpretability: the smaller the number of connections is, the higher is the transparency of the map. To this end, a novel learning approach, sparse real-coded genetic algorithms (SRCGAs), to learn FCMs is proposed. The method utilizes a density parameter to guide the learning toward a formation of maps of a certain predefined density. Comparative tests carried out for both synthetic and real-world data demonstrate that, given a suitable density estimate, the SRCGA method significantly outperforms other state-of-the-art learning methods. When the density estimate is unknown, the new method can be used in an automated fashion using a default value, and it is still able to produce models whose performance exceeds or is equal to the performance of the models generated by other methods. [ABSTRACT FROM AUTHOR]

Clustering forms one of the most visible conceptual and algorithmic framework of developing information granules. In spite of the algorithm being used, the representation of information granules–clusters is predominantly numeric (coming in the form of prototypes, partition matrices, dendrograms, etc.). In this paper, we consider a concept of granular prototypes that generalizes the numeric representation of the clusters and, in this way, helps capture more details about the data structure. By invoking the granulation–degranulation scheme, we design granular prototypes being reflective of the structure of data to a higher extent than the representation that is provided by their numeric counterparts (prototypes). The design is formulated as an optimization problem, which is guided by the coverage criterion, meaning that we maximize the number of data for which their granular realization includes the original data. The granularity of the prototypes themselves is treated as an important design asset; hence, its allocation to the individual prototypes is optimized so that the coverage criterion becomes maximized. With this regard, several schemes of optimal allocation of information granularity are investigated, where interval-valued prototypes are formed around the already produced numeric representatives. Experimental studies are provided in which the design of granular prototypes of interval format is discussed and characterized. [ABSTRACT FROM AUTHOR]

Abstract: The spatial and spatio-temporal scan statistics proposed by Kulldorff have been applied to a number of geographical disease cluster detection problems. As the shape of the scanning window used in these methods is circular or elliptic, they cannot find irregularly shaped clusters, say clusters occurring along river valleys or in cases where disease transmission is linked to the road network. In this study, we propose a more flexible geometric structure to be used as a spatial or spatio-temporal scanning window. A particle swarm optimization (PSO) is used to optimize the scanning window to determine disease clusters. We evaluated the proposed method over a number of spatial and spatio-temporal datasets (Breast cancer mortality in Northeastern US 1988–1992 and different types of cancer in New Mexico 1982–2007). Experimental results demonstrate that the introduced approach surpasses the results produced by the circular and elliptic scan statistics in terms of efficiency, especially when dealing with irregularly shaped clusters. [Copyright &y& Elsevier]

There are some variants of the widely used Fuzzy C-Means (FCM) algorithm that support clustering data distributed across different sites. Those methods have been studied under different names, like collaborative and parallel fuzzy clustering. In this study, we offer some augmentation of the two FCM-based clustering algorithms used to cluster distributed data by arriving at some constructive ways of determining essential parameters of the algorithms (including the number of clusters) and forming a set of systematically structured guidelines such as a selection of the specific algorithm depending on the nature of the data environment and the assumptions being made about the number of clusters. A thorough complexity analysis, including space, time, and communication aspects, is reported. A series of detailed numeric experiments is used to illustrate the main ideas discussed in the study. [ABSTRACT FROM AUTHOR]

Abstract: Objective: We propose and develop a concept of a granular representation of a collection of signals (patterns) where a prototype (representative) of such numeric signals is formed as a certain information granule (say, a set, fuzzy set, rough set, and alike) instead of a single numeric entity. As being more abstract, the granular format of the representative of the family of signals is more in rapport with the nature of the representation task itself. It is instrumental in quantifying the diversity of data and capture their inherent distribution characteristics. Methods and materials: In the realization of the granular representation of the signals, we introduce a certain level of granularity (supplied in advance), which in the construction of the granular representative is regarded as an essential important modeling asset. A two-phase design is developed whose ultimate goal is to optimally allocate (distribute) the predefined level of granularity to the individual elements of the universe of discourse over which the signals are described. Given the nature of the required optimization, the ensuing optimization problem is solved by engaging a machinery of population-based optimization, namely Particle Swarm Optimization (PSO). Furthermore a number of information granularity distribution protocols are proposed. The numerical experiments completed for synthetic data and ECG MIT-BIH database signals are used to demonstrate the performance of the overall optimization algorithm and quantify the effectiveness of the allocation of information granularity realized by the PSO. An area under curve (AUC) criterion is proposed as a measure to express the quality of the overall optimization framework. Results: For both synthetic as well as ECG signals, it is shown that the method endowed with the PSO identifies the best prototype and spans the lower and upper bounds of its granular counterpart. In addition to the numeric quantification of the best (optimized) granular prototype, the method helps visualizing its bounds. The relative difference in mapping performance between the best and the weakest granular prototypes is in the range of 18% (for normal ECG complexes) and over 26% in case of complexes of premature ventricular contraction. Conclusions: A complete algorithm of the construction of granular prototypes is presented. Treating the granular prototype as a template of a given class of electrocardiogram (ECG) signals, a matching process is facilitated and used as a basis for the design of signal classification algorithms. Various realizations of granular prototypes can be completed with the use of fuzzy sets or rough sets. [Copyright &y& Elsevier]

We present a new programmable neighborhood mechanism for hardware implemented Kohonen self-organizing maps (SOMs) with three different map topologies realized on a single chip. The proposed circuit comes as a fully parallel and asynchronous architecture. The mechanism is very fast. In a medium sized map with several hundreds neurons implemented in the complementary metal-oxide semiconductor 0.18 \mum technology, all neurons start adapting the weights after no more than 11 ns. The adaptation is then carried out in parallel. This is an evident advantage in comparison with the commonly used software-realized SOMs. The circuit is robust against the process, supply voltage and environment temperature variations. Due to a simple structure, it features low energy consumption of a few pJ per neuron per a single learning pattern. In this paper, we discuss different aspects of hardware realization, such as a suitable selection of the map topology and the initial neighborhood range, as the optimization of these parameters is essential when looking from the circuit complexity point of view. For the optimal values of these parameters, the chip area and the power dissipation can be reduced even by 60% and 80%, respectively, without affecting the quality of learning. [ABSTRACT FROM PUBLISHER]

Abstract: Fuzzy clustering being focused on the discovery of structure in multivariable data is of relational nature in the sense of not distinguishing between the natures of the individual variables (features) encountered in the problem. In this study, we revisit the generic approach to clustering by studying situations in which there are families of features of descriptive and functional nature whose semantics needs to be incorporated into the clustering algorithm. While the structure is determined on the basis of all features taken en-block, it is anticipated that the topology revealed in this manner would aid the effectiveness of determining values of functional features given the vector of the corresponding descriptive features. We propose an augmented distance in which the families of descriptive and predictive features are distinguished through some weighted version of the distance between patterns. The optimization of this distance is guided by a reconstruction criterion, which helps minimize the reconstruction error between the original vector of functional features and their reconstruction realized by means of descriptive features. Experimental results are offered to demonstrate the performance of the clustering and quantify the effect of reaching balance between semantically distinct families of features. [ABSTRACT FROM AUTHOR]

In this study, we present a new architecture of a granular neural network and provide a comprehensive design methodology as well as elaborate on an algorithmic setup supporting its development. The proposed neural network relates to a broad category of radial basis function neural networks (RBFNNs) in the sense that its topology involves a collection of receptive fields. In contrast to the standard architectures encountered in RBFNNs, here we form individual receptive fields in subspaces of the original input space rather than in the entire input space. These subspaces could be different for different receptive fields. The architecture of the network is fully reflective of the structure encountered in the training data which are granulated with the aid of clustering techniques. More specifically, the output space is granulated with use of K-means clustering while the information granules in the multidimensional input space are formed by using the so-called context-based fuzzy C-means, which takes into account the structure being already formed in the output space. The innovative development facet of the network involves a dynamic reduction of dimensionality of the input space in which the information granules are formed in the subspace of the overall input space which is formed by selecting a suitable subset of input variables so that this subspace retains the structure of the entire space. As this search is of combinatorial character, we use the technique of genetic optimization [genetic algorithms (GAs), to be more specific] to determine the optimal input subspaces. A series of numeric studies exploiting synthetic data and data coming from the Machine Learning Repository, University of California at Irvine, provide a detailed insight into the nature of the algorithm and its parameters as well as offer some comparative analysis. [ABSTRACT FROM AUTHOR]

The notion of a rough set that was originally proposed by Pawlak underwent a number of extensions and generalizations Dubois and Prade [4] introduced fuzzy rough sets that involve the use of rough sets and fuzzy sets within a single unified framework. Radzikowska and Kerre [5] proposed a broad family of fuzzy rough sets, referred to as (Φ, t)-fuzzy rough sets, which are determined by some implication operator (implicator) Φ and a certain t-norm. In order to describe the linguistically represented concepts coming from data available, in some information system, the concept of fuzzy rough sets are redefined and further studied in the setting of the Axiomatic Fuzzy Set (AFS) theory. Compared with the (Φ, t)-fuzzy rough sets, the advantages of AFS fuzzy rough sets are twofold. They can be directly applied to the data analysis present in any information system without resorting to the details concerning the choice of the implication Φ, t-norm, and a similarity relation S. Furthermore, such rough approximations of fuzzy concepts come with a well-defined semantics and therefore offer a sound interpretation. Some examples are included to illustrate the effectiveness of the proposed construct. It is demonstrated that the AFS fuzzy rough sets provide a far higher flexibility and effectiveness in comparison with rough sets and some of their generalizations. [ABSTRACT FROM AUTHOR]

Abstract: In this study, we develop a concept of dynamic data granulation realized in presence of incoming data organized in the form of so-called data snapshots. For each of these snapshots we reveal a structure by running fuzzy clustering. The proposed algorithm of adjustable fuzzy C-means (FCM) exhibits a number of useful features which directly associate with the dynamic nature of the underlying data: (a) the number of clusters is adjusted from one data snapshot to another in order to capture the varying structure of patterns and its complexity, (b) continuity between the consecutively discovered structures is retained, viz the clusters formed for a certain data snapshot are constructed as a result of evolving the clusters discovered in the predeceasing snapshot. We present a detailed clustering algorithm in which the mechanisms of adjustment of information granularity (the number of clusters) become the result of solutions to well-defined optimization tasks. The cluster splitting is guided by conditional fuzzy C-means (FCM) while cluster merging involves two neighboring prototypes. The criterion used to control the level of information granularity throughout the process is guided by a reconstruction criterion which quantifies an error resulting from pattern granulation and de-granulation. Numeric experiments provide a suitable illustration of the approach. [Copyright &y& Elsevier]

Focuses on the study that proposes the use of the computational intelligence approach Reinforcement Learning Algorithm (RLA) for optimizing the routing in asynchronous transfer mode (ATM) networks based on the private network-to-network interface (PNNI) standard and stochastic Estimator Learning Algorithm (SELA). Highlights of this algorithm; Overview of the PNNI standards for ATM networks used in this study; Elements of RLA; Overview of SELA; Overview of the routing algorithms that were compared in the simulation of this study;Suitability of a proposed dynamic call routing methodology that uses an efficient RLA for real-time applications in ATM networks.

Local descriptors are widely used technique of feature extraction to obtain information about both local and global properties of an object. Here, we discuss an application of the Chain Code-Based Local Descriptor to face recognition by focusing on various datasets and considering different variants of this description method. We augment the generic form of the descriptor by adding a possibility of grouping pixels into blocks, i.e., effectively describing larger neighborhoods. The results of experiments show the efficiency of the approach. We demonstrate that the obtained results are comparable or even better than those delivered by other important algorithms in the class of methods based on the Bag-of-Visual-Words paradigm. [ABSTRACT FROM AUTHOR]

Erasable itemset (EI) mining, a branch of pattern mining, helps managers to establish new plans for the development of new products. Although the problem of mining EIs was first proposed in 2009, many efficient algorithms for mining these have since been developed. However, these algorithms usually require a lot of time and memory usage. In reality, users only need a small number of EIs which satisfy a particular condition. Having this observation in mind, in this study we develop an efficient algorithm for mining EIs with subset and superset itemset constraints ( C 0 ⊆ X ⊆ C 1 ). Firstly, based on the MEI (Mining Erasable Itemsets) algorithm, we present the MEIC (Mining Erasable Itemsets with subset and superset itemset Constraints) algorithm in which each EI is checked with regard to the constraints before being added to the results. Next, two propositions supporting quick pruning of nodes that do not satisfy the constraints are established. Based on these, we propose an efficient algorithm for mining EIs with subset and superset itemset constraints (called pMEIC – p: pruning). The experimental results show that pMEIC outperforms MEIC in terms of mining time and memory usage. [ABSTRACT FROM AUTHOR]

Association rules, one of the most useful constructs in data mining, can be exerted to capture interesting dependencies between variables in large datasets. Herawan and Deris initiated the investigation of mining association rules from transactional datasets using soft set theory. Unfortunately, some existing concepts in the literature were unable to realize properly Herawan and Deris’s initial idea. This paper aims to offer further detailed insights into soft set based association rule mining. With regard to regular association rule mining using soft sets, we refine several existing concepts to improve the generality and clarity of former definitions. Regarding maximal association rule mining based on soft sets, we point out the drawbacks of some existing definitions and offer some way to rectify the problem. A number of new notions, such as transactional data soft sets, parameter-taxonomic soft sets, parameter cosets, realizations and M -realizations of parameter sets are proposed to facilitate soft set based association rule mining. Several algorithms are designed to find M -realizations of parameter sets or extract σ - M -strong and γ - M -reliable maximal association rules in parameter-taxonomic soft sets. We also present an example to illustrate potential applications of our method in clinical diagnosis. Moreover, two case studies are conducted to highlight the essentials of soft set based association rule mining approach. [ABSTRACT FROM AUTHOR]

In this study, we propose a hybrid method of face recognition by using face region information extracted from the detected face region. In the preprocessing part, we develop a hybrid approach based on the Active Shape Model (ASM) and the Principal Component Analysis (PCA) algorithm. At this step, we use a CCD (Charge Coupled Device) camera to acquire a facial image by using AdaBoost and then Histogram Equalization (HE) is employed to improve the quality of the image. ASM extracts the face contour and image shape to produce a personal profile. Then we use a PCA method to reduce dimensionality of face images. In the recognition part, we consider the improved Radial Basis Function Neural Networks (RBF NNs) to identify a unique pattern associated with each person. The proposed RBF NN architecture consists of three functional modules realizing the condition phase, the conclusion phase, and the inference phase completed with the help of fuzzy rules coming in the standard ‘if-then’ format. In the formation of the condition part of the fuzzy rules, the input space is partitioned with the use of Fuzzy C-Means (FCM) clustering. In the conclusion part of the fuzzy rules, the connections (weights) of the RBF NNs are represented by four kinds of polynomials such as constant, linear, quadratic, and reduced quadratic. The values of the coefficients are determined by running a gradient descent method. The output of the RBF NNs model is obtained by running a fuzzy inference method. The essential design parameters of the network (including learning rate, momentum coefficient and fuzzification coefficient used by the FCM) are optimized by means of Differential Evolution (DE). The proposed P-RBF NNs (Polynomial based RBF NNs) are applied to facial recognition and its performance is quantified from the viewpoint of the output performance and recognition rate. [ABSTRACT FROM AUTHOR]

Restricted Boltzmann machines and deep belief networks have been shown to perform effectively in many applications such as supervised and unsupervised learning, dimensionality reduction and feature learning. Implementing networks, which use contrastive divergence as the learning algorithm on neuromorphic hardware, can be beneficial for real-time hardware interfacing, power efficient hardware and scalability. Neuromorphic hardware which uses memristors as synapses is one of the most promising areas to achieve the above-mentioned goals. This paper presents a restricted Boltzmann machine which uses a two memristor model to emulate synaptic weights and achieves learning using contrastive divergence. [ABSTRACT FROM AUTHOR]

Abstract: In this study, we introduce a new type of coherence membership function to describe fuzzy concepts, which builds upon the theoretical findings of the Axiomatic Fuzzy Set (AFS) theory. This type of membership function embraces both the factor of fuzziness (by capturing subjective imprecision) and randomness (by referring to the objective uncertainty) and treats both of them in a consistent manner. Furthermore we propose a method to construct a fuzzy rule-based classifier using coherence membership functions. Given the theoretical developments presented there, the resulting classification systems are referred to as AFS classifiers. The proposed algorithm consists of three major steps: (a) generating fuzzy decision trees by assuming some level of specificity (detailed view) quantified in terms of threshold; (b) pruning the obtained rule-base; and (c) determining the optimal threshold resulting in a final tree. Compared with other fuzzy classifiers, the AFS classifier exhibits several essential advantages being of practical relevance. In particular, the relevance of classification results is quantified by associated confidence levels. Furthermore the proposed algorithm can be applied to data sets with mixed data type attributes. We have experimented with various data commonly present in the literature and compared the results with that of SVM, KNN, C4.5, Fuzzy Decision Trees (FDTs), Fuzzy SLIQ Decision Tree (FS-DT), FARC-HD and FURIA. It has been shown that the accuracy is higher than that being obtained by other methods. The results of statistical tests supporting comparative analysis show that the proposed algorithm performs significantly better than FDTs, FS-DT, KNN and C4.5. [Copyright &y& Elsevier]

Abstract: Mining maximal frequent itemsets is of paramount relevance in many of data mining applications. The “traditional” algorithms address this problem through scanning databases many times. The latest research has already focused on reducing the number of scanning times of databases and then decreasing the number of accessing times of I/O resources in order to improve the overall mining efficiency of maximal frequent itemsets of association rules. In this paper, we present a form of the directed itemsets graph to store the information of frequent itemsets of transaction databases, and give the trifurcate linked list storage structure of directed itemsets graph. Furthermore, we develop the mining algorithm of maximal frequent itemsets based on this structure. As a result, one realizes scanning a database only once, and improves storage efficiency of data structure and time efficiency of mining algorithm. [Copyright &y& Elsevier]

Abstract: There is a remarkably rich landscape of fuzzy clustering and ensuing design procedures of information granules. In a nutshell, fuzzy clustering (and clustering, in general) leads to direction-free constructs meaning that there is no clear distinction between input and output variables. In the framework of fuzzy modeling, information granules are used in the development of input–output mapping and from this perspective it becomes beneficial to consider the aspect of directionality in the construction of information granules (fuzzy sets) in the input space. Conditional fuzzy C-means clustering comes as one of the algorithmically viable alternatives using which we construct fuzzy sets over the input space in presence of supervision coming in the form of structure of data distributed over the output space. In this paper, presented is a new clustering method in which we use the ambiguity index to express the boundaries of the clusters. The design is illustrated with the aid of several numeric examples that provide a detailed insight into the performance of the fuzzy models formed in this manner and also highlight several crucial design issues. [ABSTRACT FROM AUTHOR]