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Various data mining methods have been proposed to handle large-scale data and discover interesting knowledge hidden in the data. Maximal frequent pattern mining is one of the data mining techniques suggested to solve the fatal problem of traditional frequent pattern mining approach. While traditional approach may extract an enormous number of pattern results according to threshold settings, maximal frequent pattern mining approach mines a smaller number of representative patterns, which allow users to analyze given data more efficiently. In this paper, we describe various recent maximal frequent pattern mining methods using additional factors and conduct performance evaluation in order to analyze their detailed characteristics.

A core issue of the association rule extracting process in the data mining field is to find the frequent patterns in the database of operational transactions. If these patterns discovered, the decision making process and determining strategies in organizations will be accomplished with greater precision. Frequent pattern is a pattern seen in a significant number of transactions. Due to the properties of these data models which are unlimited and high-speed production, these data could not be stored in memory and for this reason it is necessary to develop techniques that enable them to be processed online and find repetitive patterns. Several mining methods have been proposed in the literature which attempt to efficiently extract a complete or a closed set of different types of frequent patterns from a dataset. In this paper, a method underpinned upon Cellular Learning Automata (CLA) is presented for mining frequent itemsets. The proposed method is compared with Apriori, FP-Growth and BitTable methods and it is ultimately concluded that the frequent itemset mining could be achieved in less running time. The experiments are conducted on several experimental data sets with different amounts of minsup for all the algorithms as well as the presented method individually. Eventually the results prod to the effectiveness of the proposed method.

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.

The extraction of similar features based on quality is called pattern discovery to the huge number of terms, phrases and noise. Identifying the better pattern discovery is the major problem to extract the accurate information from the text documents because of the noise and unwanted data present in the text documents. In this paper, pattern discovery is used to find the frequent item sets and reducing the noise from text documents and implement the advanced pattern discovery approach. In this paper, for implementation we use .txt files with unstructured data to find the efficient patterns.

Recently, association rule mining plays a vital role in knowledge discovery in database. In fact, in most cases, the real datasets lead to a very large number of rules, which do not allow users to make their own selection of the most relevant. The difficult task is mining useful and non-redundant rules. Several approaches have been proposed, such as rule clustering, informative cover method and quality measurements. Another way to selecting relevant association rules, we believe that it is necessary to integrate a decisional approach within the knowledge discovery process. Therefore, in this paper, we propose an approach to discover a category of relevant association rules based on multi-criteria analysis. In other side, the general process of association rules extraction becomes more and more complex, to solve such problem, we also proposed a multi-agent system for modeling the different process of our proposed approach. Therefore, we conclude our work by an empirical study applied to a set of banking data to illustrate the performance of our approach.

Association rule mining was first introduced to examine patterns among frequent items. The original motivation for seeking these rules arose from need to examine customer purchasing behaviour in supermarket transaction data. It seeks to identify combinations of items or itemsets, whose presence in a transaction affects the likelihood of the presence of another specific item or itemsets. In recent years, there has been an increasing demand for rare association rule mining. Detecting rare patterns in data is a vital task, with numerous high-impact applications including medical, finance, and security. This survey aims to provide a general, comprehensive, and structured overview of the state-of-the-art methods for rare pattern mining. We investigate the problems in finding rare rules using traditional association rule mining. As rare association rule mining has not been well explored, there is still specific groundwork that needs to be established. We will discuss some of the major issues in rare association rule mining and also look at current algorithms. As a contribution, we give a general framework for categorizing algorithms: Apriori and Tree based. We highlight the differences between these methods. Finally, we present several real-world application using rare pattern mining in diverse domains. We conclude our survey with a discussion on open and practical challenges in the field.

Association rule mining is an iterative and interactive process of discovering valid, novel, useful, understandable and hidden associations from the massive database. The Colossal databases require powerful and intelligent tools for analysis and discovery of frequent patterns and association rules. Several researchers have proposed the many algorithms for generating item sets and association rules for discovery of frequent patterns, and minning of the association rules. These proposals are validated on static data. A dynamic database may introduce some new association rules, which may be interesting and helpful in taking better business decisions. In association rule mining, the validation of performance and cost of the existing algorithms on incremental data are less explored. Hence, there is a strong need of comprehensive study and in-depth analysis of the existing proposals of association rule mining. In this paper, the existing tree-based algorithms for incremental data mining are presented and compared on the baisis of number of scans, structure, size and type of database. It is concluded that the Can-Tree approach dominates the other algorithms such as FP-Tree, FUFP-Tree, FELINE Alorithm with CATS-Tree etc.This study also highlights some hot issues and future research directions. This study also points out that there is a strong need for devising an efficient and new algorithm for incremental data mining.

Time series analysis is an important data mining task in areas such as the stock market and petroleum industry. One interesting problem in knowledge discovery is the detection of previously unknown frequent patterns. With the existing types of patterns, some similar subsequences are overlooked or dissimilar ones are matched. In this paper, we define patterns with weak-wildcard gaps to represent subsequences with noise and shift, and design efficient algorithms to obtain frequent and strong patterns. First, we convert a numeric time series into a sequence according to the data fluctuation. Second, we define the pattern mining with weak-wildcard gaps problem, where a weak-wildcard matches any character in an alphabet subset. Third, we design an Apriori-like algorithm with an efficient pruning technique to obtain frequent and strong patterns. Experimental results show that our algorithm is efficient and can discover frequent and strong patterns.

This thesis focuses on mining association rules on multivariate time series. Com-mon association rule mining algorithms can usually only be applied to transactional data, and a typical application ...

The discovery of patterns that accurately discriminate one class label from another remains a challenging data mining task. Subgroup discovery (SD) is one of the frameworks that enables to elicit such interesting hypotheses from labeled data. A question remains fairly open: How to select an accurate heuristic search technique when exhaustive enumeration of the pattern space is infeasible? Existing approaches make use of beam-search, sampling and genetic algorithms for discovering a pattern set that is non-redundant and of high quality w.r.t. a pattern quality measure. We argue that such approaches produce pattern sets that lack of diversity: Only few patterns of high quality, and different enough, are discovered. Our main contribution is then to formally define pattern mining as a game and to solve it with Monte Carlo tree search (MCTS). It can be seen as an exhaustive search guided by random simulations which can be stopped early (limited budget) by virtue of its best-first search property. We show through a comprehensive set of experiments how MCTS enables the anytime discovery of a diverse pattern set of high quality. It outperforms other approaches when dealing with a large pattern search space and for different quality measures. Thanks to its genericity, our MCTS settings can be used for SD but also for many other pattern mining tasks., This article has been accepted for publication in the journal \textit{Data Mining and Knowledge Discovery} (December 5th, 2017)

Traffic accident is one of the major cause of death worldwide for the last several decades. According to the statistics of world health organization, approximately 1.24 million deaths occurred on the world's roads in 2010. In order to reduce future traffic accident, multipronged approaches have been adopted including traffic regulations, injury-reducing technologies, driving training program and so on. Records on traffic accidents are generated and maintained for this purpose. To make these records meaningful and effective, it is necessary to analyze relationship between traffic accident and related factors including vehicle design, road design, weather, driver behavior etc. Insight derived from these analysis can be used for accident prevention approaches. Traffic accident data mining is an activity to find useful knowledges about such relationship that is not well-known and user may interested in it. Many studies about mining accident data have been reported over the past two decades. Most of studies mainly focused on predict risk of accident using accident related factors. Supervised learning methods like decision tree, logistic regression, k-nearest neighbor, neural network are used for these prediction. However, derived prediction model from these algorithms are too complex to understand for human itself because the main purpose of these algorithms are prediction, not explanation of the data. Some of studies use unsupervised clustering algorithm to dividing the data into several groups, but derived group itself is still not easy to understand for human, so it is necessary to do some additional analytic works. Rule based learning methods are adequate when we want to derive comprehensive form of knowledge about the target domain. It derives a set of if-then rules that represent relationship between the target feature with other features. Rules are fairly easy for human to understand its meaning therefore it can help provide insight and comprehensible results for human. Association rule learning methods and subgroup discovery methods are representing rule based learning methods for descriptive task. These two algorithms have been used in a wide range of area from transaction analysis, accident data analysis, detection of statistically significant patient risk groups, discovering key person in social communities and so on. We use both the association rule learning method and the subgroup discovery method to discover useful patterns from a traffic accident dataset consisting of many features including profile of driver, location of accident, types of accident, information of vehicle, violation of regulation and so on. The association rule learning method, which is one of the unsupervised learning methods, searches for frequent item sets from the data and translates them into rules. In contrast, the subgroup discovery method is a kind of supervised learning method that discovers rules of user specified concepts satisfying certain degree of generality and unusualness. Depending on what aspect of the data we are focusing our attention to, we may combine different multiple relevant features of interest to make a synthetic target feature, and give it to the rule learning algorithms. After a set of rules is derived, some postprocessing steps are taken to make the ruleset more compact and easier to understand by removing some uninteresting or redundant rules. We conducted a set of experiments of mining our traffic accident data in both unsupervised mode and supervised mode for comparison of these rule based learning algorithms. Experiments with the traffic accident data reveals that the association rule learning, in its pure unsupervised mode, can discover some hidden relationship among the features. Under supervised learning setting with combinatorial target feature, however, the subgroup discovery method finds good rules much more easily than the association rule learning method that requires a lot of efforts to tune the parameters.

Graphical abstractDisplay Omitted HighlightsWe propose a novel approach to adaptive relational association rule mining.ARARM algorithm is capable of adaptively mining relational association rules.It is applied when the feature set characterizing the objects to be mined increases over time.Experiments show a significant reduction in running time compared to the non-adaptive method.ARARM is faster with 53% (in average) than the non-adaptive method. The paper focuses on the adaptive relational association rule mining problem. Relational association rules represent a particular type of association rules which describe frequent relations that occur between the features characterizing the instances within a data set. We aim at re-mining an object set, previously mined, when the feature set characterizing the objects increases. An adaptive relational association rule method, based on the discovery of interesting relational association rules, is proposed. This method, called ARARM (Adaptive Relational Association Rule Mining) adapts the set of rules that was established by mining the data before the feature set changed, preserving the completeness. We aim to reach the result more efficiently than running the mining algorithm again from scratch on the feature-extended object set. Experiments testing the method's performance on several case studies are also reported. The obtained results highlight the efficiency of the ARARM method and confirm the potential of our proposal.

The effectiveness of mining association rules is a significant field of Knowledge Discovery in Databases (KDD). The Apriori algorithm is a classical algorithm in mining association rules. This paper presents an improved method for Apriori and Frequent Pattern algorithms to increase the efficiency of generating association rules. This algorithm adopts a new method to decrease the redundant generation of sub-itemsets during pruning the candidate itemsets, which can form directly the set of frequent itemset and remove candidates having a subset that is not frequent in the meantime. This algorithm can raise the probability of obtaining information in scanning database and reduce the potential scale of itemsets

There are many data mining techniques have been proposed for mining useful patterns from documents. Still, how to effectively use and update discovered patterns is open for future research , especially in the field of text mining. As most existing text mining methods adopted term-based approaches, they all suffer from the problems of polysemy(words have multiple meanings) and synonymy(multiple words have same meaning). People have held hypothesis that pattern-based approaches should perform better than the term-based, but many experiments does not support this hypothesis. This paper presents an innovative and effective pattern discovery technique which includes the processes of pattern deploying and pattern matching, to improve the effective use of discovered patterns.

Data mining is defined as the computational process of analyzing large amounts of data in order to extract patterns and useful information. In the last few decades, data mining has been widely recognized as a powerful yet versatile data-analysis tool in a variety of fields: information technology in primis, but also clinical medicine, sociology, physics. In this technical note we provide a high-level overview of the most prominent tasks and methods that form the basis of data mining. The note also focuses on some of the most recent yet promising interdisciplinary aspects of data mining.

In the field of data mining, deriving set of rules from large dataset is primary objective. Association rule mining plays very important role to discover interesting rules according to domain knowledge specified as a minimum support threshold. The efficiency of association rule algorithm can be increased by focusing on reducing number of passes, sampling, parallel execution and constraint based association rule mining. In this paper various ways to enhance performance of association rule mining are discussed.

Sequential pattern mining is a data mining technique that aims to extract and analyze frequent subsequences from sequences of events or items with time constraint. Sequence data mining was introduced in 1995 with the well-known Apriori algorithm. The algorithm studied the transactions through time, in order to extract frequent patterns from the sequences of products related to a customer. Later, this technique became useful in many applications: DNA researches, medical diagnosis and prevention, telecommunications, etc. GSP, SPAM, SPADE, PrefixSPan and other advanced algorithms followed. View the evolution of data mining techniques based on sequential data, this paper discusses the multiple extensions of Sequential Pattern mining algorithms. We classified the algorithms into Sequential Pattern mining, Sequential rule mining and Sequence prediction with their extensions. The classification is presented in a tree at the end of the paper.

Mining association rules plays an important role in data mining and knowledge discovery since it can reveal strong associations between items in databases. Nevertheless, an important problem with traditional association rule mining methods is that they can generate a huge amount of association rules depending on how parameters are set. However, users are often only interested in finding the strongest rules, and do not want to go through a large amount of rules or wait for these rules to be generated. To address those needs, algorithms have been proposed to mine the top-k association rules in databases, where users can directly set a parameter k to obtain the k most frequent rules. However, a major issue with these techniques is that they remain very costly in terms of execution time and memory. To address this issue, this paper presents a novel algorithm named ETARM (Efficient Top-k Association Rule Miner) to efficiently find the complete set of top-k association rules. The proposed algorithm integrates two novel candidate pruning properties to more effectively reduce the search space. These properties are applied during the candidate selection process to identify items that should not be used to expand a rule based on its confidence, to reduce the number of candidates. An extensive experimental evaluation on six standard benchmark datasets show that the proposed approach outperforms the state-of-the-art TopKRules algorithm both in terms of runtime and memory usage.

Mining textual patterns in news, tweets, papers, and many other kinds of text corpora has been an active theme in text mining and NLP research. Previous studies adopt a dependency parsing-based pattern discovery approach. However, the parsing results lose rich context around entities in the patterns, and the process is costly for a corpus of large scale. In this study, we propose a novel typed textual pattern structure, called meta pattern, which is extended to a frequent, informative, and precise subsequence pattern in certain context. We propose an efficient framework, called MetaPAD, which discovers meta patterns from massive corpora with three techniques: (1) it develops a context-aware segmentation method to carefully determine the boundaries of patterns with a learnt pattern quality assessment function, which avoids costly dependency parsing and generates high-quality patterns; (2) it identifies and groups synonymous meta patterns from multiple facets---their types, contexts, and extractions; and (3) it examines type distributions of entities in the instances extracted by each group of patterns, and looks for appropriate type levels to make discovered patterns precise. Experiments demonstrate that our proposed framework discovers high-quality typed textual patterns efficiently from different genres of massive corpora and facilitates information extraction., 9 pages

A method for classifying objects into categories and indexing is proposed to implement object recognition. The relational measurements such as the distance between two points, color comparison is encoded by the attributed relational graph (ARG) representation to provide one-to-one correspondence between models and object features. If the contour is traversed counterclockwise, a sequence can be formed to roughly represent the shape of the object. Second, for a set of similar objects, an association rule mining approach is used to extract the feature pattern, which is the common subsequences of those objects, to represent their common shapes. Third, for a testing image containing an object, its sequence can be computed according to the first phase. Then, the testing sequence can be used to match feature patterns generated in the second phase by a dynamic programming approach. If the testing sequence approximately contains a feature pattern, the object is identified as the object represented by the feature pattern. The experiment results show that the proposed method can use a limited number of training data and the objects with slight variations also can be recognized since feature patterns can mainly represent the shapes of these objects.

International audience; Data mining is the study of how to extract information from data and express it as useful knowledge. One of its most important subfields, pattern mining, involves searching and enumerating interesting patterns in data. Various aspects of pattern mining are studied in the theory of computation and statistics. In the last decade, the pattern mining community has witnessed a sharp shift from efficiency-based approaches to methods which can extract more meaningful patterns. Recently, new methods adapting results from studies of economic efficiency and multi-criteria decision analyses such as Pareto efficiency, or skylines, have been studied. Within pattern mining, this novel line of research allows the easy expression of preferences according to a dominance relation. This approach is useful from a user-preference point of view and tends to promote the use of pattern mining algorithms for non-experts. We present a significant extension of our previous work [1,2] on the discovery of skyline patterns (or "skypatterns") based on the theoretical relationships with condensed representations of patterns. We show how these relationships facilitate the computation of skypatterns and we exploit them to propose a flexible and efficient approach to mine skypatterns using a dynamic constraint satisfaction problems (CSP) framework. We present a unified methodology of our different approaches towards the same goal. This work is supported by an extensive experimental study allowing us to illustrate the strengths and weaknesses of each approach.

In this research study, we analyze the performance of bio inspired classification approaches by selecting Ant-Miners (Ant-Miner, cAnt_Miner, cAnt_Miner2 and cAnt_MinerPB) for the discovery of classification rules in terms of accuracy, terms per rule, number of rules, running time and model size discovered by the corresponding rule mining algorithm. Classification rule discovery is still a challenging and emerging research problem in the field of data mining and knowledge discovery. Rule based classification has become cutting edge research area due to its importance and popular application areas in the banking, market basket analysis, credit card fraud detection, costumer behaviour, stock market prediction and protein sequence analysis. There are various approaches proposed for the discovery of classification rules like Artificial Neural Networks, Genetic Algorithm, Evolutionary Programming, SVM and Swarm Intelligence. This research study is focused on classification rule discovery by Ant Colony Optimization. For the performance analysis, Myra Tool is used for experiments on the 18 public datasets (available on the UCI repository). Data sets are selected with varying number of instances, number of attributes and number of classes. This research paper also provides focused survey of Ant-Miners for the discovery of classification rules.

Due to fast growth of temporal databases has made temporal data mining mandatory for knowledge discovery. Temporal association rule classification, a sub-task of temporal mining, integrates association rule mining and classification. The growth and increased complexities in temporal databases have necessitated this research work to propose techniques that enhance the process of associative mining and classification. Hierarchical partitioning with frequent pattern list with multiple projection pruning and 2-Step Associative rule Classification with Temporal characteristic (HM2ACT) is proposed to solve the issues and designed enhanced temporal association rule classification algorithm. The experimental results demonstrated that the proposed algorithm produces high quality rules and improved classification performance.

This chapter provides frameworks for association and classification rules discovery from relational data. The frameworks are specialized versions of the general granular computing framework for mining relational data. The chapter also shows that the time complexity of generating rules from a granular representation of relational data can be decreased.

With increasing in amount of available data, researchers try to propose new approaches for extracting useful knowledge. Association Rule Mining (ARM) is one of the main approaches that became popular in this field. It can extract frequent rules and patterns from a database. Many approaches were proposed for mining frequent patterns; however, heuristic algorithms are one of the promising methods and many of ARM algorithms are based on these kinds of algorithms. In this paper, we improve our previous approach, ARMICA, and try to consider more parameters, like the number of database scans, the number of generated rules, and the quality of generated rules. We compare the proposed method with the Apriori, ARMICA, and FP-growth and the experimental results indicate that ARMICA-Improved is faster, produces less number of rules, generates rules with more quality, has less number of database scans, it is accurate, and finally, it is an automatic approach and does not need predefined minimum support and confidence values.

association rules are mine useful information form large dataset. Traditional association mining methods generate frequent rules based on frequent itemsets with reference of minimum support and minimum confidence threshold which specified by user. It called as support-confidence framework. As many of generated rules are of no use, further analysis is essential to find interesting Rules. A rule that contains rare items can consider as rare association rule. Rare Association Rules Represent unpredictable or unknown association, so it is more interesting than frequent association rule. Rare association rule mining provides relationship between items which occurs uncommonly. This paper presents brief survey in the area of rare association rule mining. Keywordspattern, support, confidence, Rare Items

mining is a process of extracting knowledge from underlying huge multidimensional data. Data mining techniques discovers hidden patterns from a given data. Classification is one of the techniques of data mining. Data Classification consists of categorization of data under the known class labels for its most effective and efficient utilization. There are different algorithms available for classification. Association rule mining is used to generate the rules for strongly associated attributes. It helps to uncover the association between seemingly unrelated attributes. Association rules are identified by analyzing patterns which satisfies the confidence and support criteria. This paper explains pre-processing for a given bank data set along with classification and association rule mining. Association followed by classification method helps in finding the noisy data attributes. Experimental setup uses WEKA tool for data mining. WEKA is a collection of machine learning algorithms for data mining tasks. Experiment has shown that classification with guidance of strongly supported rules from association rule mining helps in removal of noise and has increased the accuracy of classifier for a given data set.

Data Mining is a way of obtaining undetected patterns or facts from massive amount of data in a database. Association rule mining is a major technique in the area of data mining. Association rule mining finds frequent itemsets from a set of transactional databases. Apriori algorithm is one of the earliest algorithm of association rule mining. Apriori employs an iterative approach known as levelwise search. In this paper, we have presented a survey of most recent work that has been done by researchers in Association rule based mining using Apriori algorithm.

Association rule mining, an important data mining technique, has been widely focused on the extraction of frequent patterns. Nevertheless, in some application domains it is interesting to discover patterns that do not frequently occur, even when they are strongly related. More specifically, this type of relation can be very appropriate in e-learning domains due to its intrinsic imbalanced nature. In these domains, the aim is to discover a small but interesting and useful set of rules that could barely be extracted by traditional algorithms founded in exhaustive search-based techniques. In this paper, we propose an evolutionary algorithm for mining rare class association rules when gathering student usage data from a Moodle system. We analyse how the use of different parameters of the algorithm determine the rule characteristics, and provides some illustrative examples of them to show their interpretability and usefulness in e-learning environments. We also compare our approach to other existing algorithms for mining both rare and frequent association rules. Finally, an analysis of the rules mined is presented, which allows information about students' unusual behaviour regarding the achievement of bad or good marks to be discovered.

Data mining has become a process of significant interest in recent years due to explosive rate of the accumulation of data. It is used to discover potentially valuable implicit knowledge from the large transactional databases. Association rule mining is one of the well known techniques of data mining. It typically aims at discovering associations between attributes in the large databases. The first and the most influential traditional algorithm for association rule discovery is Apriori. Multiple scans of database, generation of large number of candidates item set and discovery of interesting rules are the main challenging issues for the improvement of Apriori algorithm. Therefore in order to decrease the multiple scanning of database, a new method of association rule mining using pattern generation is proposed in this paper. This method involves three steps. First, patterns are generated using items from the transaction database. Second, frequent item set is obtained using these patterns. Finally association rules are derived. The performance of this method is evaluated with the traditional Apriori algorithm. It shows that behavior of the proposed method is much more similar to Apriori algorithm with less memory space and reduction in multiple times scanning of database. Thus it is more efficient than the traditional Apriori algorithm.

Cloud computing is the latest trend in IT technical development, the importance of cloud databases has been widely acknowledged. There are numerous data in the cloud database and among these data, much potential and valuable knowledge are implicit. The key point is to discover and pick up the useful knowledge automatically. An association rule is one of the main models in mining out these data, and it mainly focuses on the relationship among different areas in the data. This paper puts forward the basic model of data mining based on association rules in cloud database and introduces corresponding mining algorithms.

Many data mining techniques have been proposed for mining useful patterns in text documents. However, how to effectively use and update discovered patterns is still an open researc h issue, especially in the domain of text mining. S ince most existing text mining methods adopted term-based approaches, they all suf fer from the problems of polysemy and synonymy. Ove r the years, people have often held the hypothesis that pattern (or phrase)- based approaches should perform better than the ter m-based ones, but many experiments do not support this hypothesis. Propose d work presents an innovative and effective pattern discovery technique which includes the processes of pattern deploying and pat tern evolving, to improve the effectiveness of usin g and updating discovered patterns for finding relevant and interesting infor mation.

Aims: Frequent pattern mining is one of the imperative tasks in the data mining. The soft computing techniques such as neural network, fuzzy logic have potential to be used in frequent pattern mining since these powerful tools efficiently model data which is also an essential part of mining. The proposed paper aims to provide efficient mining solution using auto-associative memory neural network to efficiently traverse and reduce the search space, and to reduce the I/O computations. It also aims to keep balance in computational and resource efficiency. Methodology: This paper proposes an efficient algorithm for mining frequent patterns using auto-associative memory. Auto-associative memory is best suitable artificial neural network (ANN) approach for association rule mining as it stores associations among the patterns. In the proposed system auto-associative memory based on the correlation matrix memory (CMM) is used to find the frequent patterns. The proposed work introduces novel learning and recall algorithms using CMM for mining frequent patterns efficiently. The proposed learning algorithm reduces the search space tremendously for recall mechanism. The proposed recall algorithm uses only frequent 1-patterns and frequent 2-patterns for determining all other frequent patterns, reducing the number of I/O

Data mining is gaining importance due to huge amount of data available. Retrieving information from the warehouse is not only tedious but also difficult in some cases. The most important usage of data mining is customer segmentation in marketing, shopping cart analyzes, management of customer relationship, campaign management, Web usage mining, text mining, player tracking and so on. In data mining, association rule mining is one of the important techniques for discovering meaningful patterns from large collection of data. Discovering frequent itemsets play an important role in mining association rules, sequence rules, web log mining and many other interesting patterns among complex data. This paper presents a literature review on different techniques for mining frequent itemsets.

Pattern recognition is seen as a major challenge within the field of data mining and knowledge discovery. For the work in this paper, we have analyzed a range of widely used algorithms for finding frequent patterns with the purpose of discovering how these algorithms can be used to obtain frequent patterns over large transactional databases. This has been presented in the form of a comparative study of the following algorithms: Apriori algorithm, Frequent Pattern (FP) Growth algorithm, Rapid Association Rule Mining (RARM), ECLAT algorithm and Associated Sensor Pattern Mining of Data Stream (ASPMS) frequent pattern mining algorithms. This study also focuses on each of the algorithm's strengths and weaknesses for finding patterns among large item sets in database systems.

Process discovery is the learning task that entails the construction of process models from event logs of information systems. Typically, these event logs are large data sets that contain the process executions by registering what activity has taken place at a certain moment in time. By far the most arduous challenge for process discovery algorithms consists of tackling the problem of accurate and comprehensible knowledge discovery from highly flexible environments. Event logs from such flexible systems often contain a large variety of process executions which makes the application of process mining most interesting. However, simply applying existing process discovery techniques will often yield highly incomprehensible process models because of their inaccuracy and complexity. With respect to resolving this problem, trace clustering is one very interesting approach since it allows to split up an existing event log so as to facilitate the knowledge discovery process. In this paper, we propose a novel trace clustering technique that significantly differs from previous approaches. Above all, it starts from the observation that currently available techniques suffer from a large divergence between the clustering bias and the evaluation bias. By employing an active learning inspired approach, this bias divergence is solved. In an assessment using four complex, real-life event logs, it is shown that our technique significantly outperforms currently available trace clustering techniques.

Frequent itemset (or frequent pattern) mining is a very important issue within the data mining field. Both, syntactic simplicity and descriptive potential, are the key features of the itemset-based pattern which have led to its widespread use in a growing number of real-life domains. Some of the most representative algorithms for mining this kind of pattern are Apriori-like algorithms and, therefore, the number of patterns obtained under normal conditions is very large, making the process of evaluation and interpretation quite difficult. This problem is compounded if we consider that knowledge discovery is an iterative process, and the change in the parameters of the preprocessing techniques or the mining algorithm can lead to significant changes in the result. In this paper, we propose a method based on Shafer's Theory of Evidence which uses two information measures for the quality evaluation of the set of frequent patterns. From a practical point of view, the main goal is to select, for a given database, the best preprocessing technique that lead to the discovery of useful knowledge. Nevertheless, the underlying idea is to propose a formal method to assess, objectively, sets of frequent patterns, seen as belief structures, in terms of certainty in the information they represent.

Frequent pattern mining has become one of the most popular data mining approaches for the analysis of purchasing patterns. There are techniques such as Apriority and FP-Growth, which were typically restricted to a single concept level. We extend our research to study Multi - level frequent patterns in multi-level environments. Mining Multi-level frequent pattern may lead to the discovery of mining patterns at different levels of hierarchy. In this study, we describe the main techniques used to solve these problems and give a comprehensive survey of the most influential algorithms That were proposed during the last decade.

Contrast mining is one of fairly new hot data mining topics.Contrast mining focuses on knowledge that describes differences between classes and conditions,or describes changes over time.Contrast mining aims at developing techniques to discover patterns or models that contrast,and characterize multiple datasets associated with different classes or conditions.Contrast mining has wide applications in reality,due to its ability of simplifying problems and classifying accurately.Research on the mining and application of emerging patterns represents a major direction of contrast mining.This paper provided a survey of such issue.More specifically,after introducing the background,basic concepts and principles of emerging patterns,the paper analyzed the mining methods of emerging patterns,discussed extended definitions of emerging patterns and their mining,stated methods for constructing emerging pattern based classifiers,and illustrated applications of emerging pattern in several real-world fields.Finally,this paper gave out some topics for future research on emerging pattern based contrast mining.

Association rule mining is a computationally expensive task. Despite the huge processing cost, it has gained tremendous popularity due to the usefulness of association rules. Several efficient algorithms can be found in the literature. This paper provides a comprehensive survey on the state-of-the-art algorithms for association rule mining, specially when the data sets used for rule mining are not static. Addition of new data to a data set may lead to additional rules or to the modification of existing rules. Finding the association rules from the whole data set may lead to significant waste of time if the process has started from the scratch. Several algorithms have been evolved to attend this important issue of the association rule mining problem. This paper analyzes some of them to tackle the incremental association rule mining problem. © 2013 Wiley Periodicals, Inc.

To date, association rule mining has mainly focused on the discovery of frequent patterns. Nevertheless, it is often interesting to focus on those that do not frequently occur. Existing algorithms for mining this kind of infrequent patterns are mainly based on exhaustive search methods and can be applied only over categorical domains. In a previous work, the use of grammar-guided genetic programming for the discovery of frequent association rules was introduced, showing that this proposal was competitive in terms of scalability, expressiveness, flexibility and the ability to restrict the search space. The goal of this work is to demonstrate that this proposal is also appropriate for the discovery of rare association rules. This approach allows one to obtain solutions within specified time limits and does not require large amounts of memory, as current algorithms do. It also provides mechanisms to discard noise from the rare association rule set by applying four different and specific fitness functions, which are compared and studied in depth. Finally, this approach is compared with other existing algorithms for mining rare association rules, and an analysis of the mined rules is performed. As a result, this approach mines rare rules in a homogeneous and low execution time. The experimental study shows that this proposal obtains a small and accurate set of rules close to the size specified by the data miner.

Sequences of events, items, or tokens occurring in an ordered metric space appear often in data and the requirement to detect and analyze frequent subsequences is a common problem. Sequential Pattern Mining arose as a subfield of data mining to focus on this field. This article surveys the approaches and algorithms proposed to date.

Scientific progress in recent years has led to the generation of huge amounts of biological data, most of which remains unanalyzed. Mining the data may provide insights into various realms of biology, such as finding co-occurring biosequences, which are essential for biological data mining and analysis. Data mining techniques like sequential pattern mining may reveal implicitly meaningful patterns among the DNA or protein sequences. If biologists hope to unlock the potential of sequential pattern mining in their field, it is necessary to move away from traditional sequential pattern mining algorithms, because they have difficulty handling a small number of items and long sequences in biological data, such as gene and protein sequences. To address the problem, we propose an approach called Depth-First SPelling (DFSP) algorithm for mining sequential patterns in biological sequences. The algorithm’s processing speed is faster than that of PrefixSpan, its leading competitor, and it is superior to other sequential pattern mining algorithms for biological sequences.

Combined mining is a technique for analyzing object relations and pattern relations, and for extracting and constructing actionable knowledge (patterns or exceptions). Although combined patterns can be built within a single method, such as combined sequential patterns by aggregating relevant frequent sequences, this knowledge is composed of multiple constituent components (the left hand side) from multiple data sources, which are represented by different feature spaces, or identified by diverse modeling methods. In some cases, this knowledge is also associated with certain impacts (influence, action, or conclusion, on the right hand side). This paper presents an abstract high-level picture of combined mining and the combined patterns from the perspective of object and pattern relation analysis. Several fundamental aspects of combined pattern mining are discussed, including feature interaction, pattern interaction, pattern dynamics, pattern impact, pattern relation, pattern structure, pattern paradigm, pattern formation criteria, and pattern presentation (in terms of pattern ontology and pattern dynamic charts). We also briefly illustrate the concepts and discuss how they can be applied to mining complex data for complex knowledge in either a multifeature, multisource, or multimethod scenario. © 2013 Wiley Periodicals, Inc.

Efficient algorithm to discover frequent pattern are crucial in data mining research. Finding frequent itemsets is computationally the most expensive step in association rule discovery .To address these issues we discuss popular techniques for finding frequent itemsets in efficient way. In this paper we provide the survey list of existing frequent itemsets mining techniques and proposing new procedure which having some advantages by comparing with the other algorithms. Keywords— Association rules, data mining, frequent itemsets ,FPM, minimum support. I. INTRODUCTION

This paper proposes a new approach to mine context based positive and negative spatial association rules as they might be applied to hydrocarbon prospection. Many researchers are currently using an Apriori algorithm on spatial databases but this algorithm does not utilize the strengths of positive and negative association rules and of time series analysis, hence it misses the discovery of very interesting and useful associations present in the data. In dense spatial databases, the number of negative association rules is much higher compared to the positive rules which need exploitation. Using positive and negative association rule discovery and then pruning out the uninteresting rules consumes resources without much improvement in the overall accuracy of the knowledge discovery process. The associations among different objects and lattices are strongly dependent upon the context, particularly where context is the state of entity, environment or action. We propose an approach to spatial association rule mining from datasets projected at a temporal bar in which the contextual situation is considered while generating positive and negative frequent itemsets. An extended algorithm based on the Apriori approach is developed and compared with existing spatial association rule algorithms. The algorithm for positive and negative association rule mining is based on Apriori algorithm which is further extended to include context variable and simulate temporal series spatial inputs. The numerical evaluation shows that our algorithm is more efficient at generating specific, reliable and robust information than traditional algorithms.

Data mining discovers hidden pattern in data sets and association between the patterns. In this association rule mining is one of the technique used to achieve the objective of data mining. These rules are effectively used to uncover unknown relationships producing result that can give us a basis for forecasting and decision making. To discover these rules we have to find out the frequent item sets because these item sets are the building blocks to obtain Association rules with a given confidence and support. In this paper we theoretical analyses the extraction for frequent item sets and compare these algorithm.