A survey of itemset mining

Constraint Programming is becoming competitive for solving certain data-mining problems largely due to the development of global constraints. We introduce the CoverSize constraint for itemset mining problems, a global constraint for counting and constraining the number of transactions covered by the itemset decision variables. We show the relation of this constraint to the well-known table constraint, and our filtering algorithm internally uses the reversible sparse bitset data structure recently proposed for filtering table. Furthermore, we expose the size of the cover as a variable, which opens up new modelling perspectives compared to an existing global constraint for (closed) frequent itemset mining. For example, one can constrain minimum frequency or compare the frequency of an itemset in different datasets as is done in discriminative itemset mining. We demonstrate experimentally on the frequent, closed and discriminative itemset mining problems that the CoverSize constraint with reversible sparse bitsets allows to outperform other CP approaches.

Discovering interesting patterns in transactional databases is often a challenging area by the length of patterns and number of transactions in data mining, which is prohibitively expensive in both time and space. Closed itemset mining is introduced from traditional frequent pattern mining and having its own importance in data mining applications. Recently, regular itemset mining gained lot of attention in data mining research because of its occurrence behavior. In this paper we propose a new method called RCP-method (regular-closed pattern method) to mine regular-closed itemsets in transactional database by using vertical data format. Our RCP-method generates complete set of regular-closed patterns in transactional databases for a user given regularity threshold and support. Our experimental results show that this method is efficient in memory and execution time.

In this work we focus on the problem of frequent itemset mining on large, out-of-core data sets. After presenting a characterization of existing out-of-core frequent itemset mining algorithms and their drawbacks, we introduce our efficient, highly scalable solution. Presented in the context of the FPGrowth algorithm, our technique involves several novel I/O-conscious optimizations, such as approximate hash-based sorting and blocking, and leverages recent architectural advancements in commodity computers, such as 64-bit processing. We evaluate the proposed optimizations on truly large data sets,up to 75GB, and show they yield greater than a 400-fold execution time improvement. Finally, we discuss the impact of this research in the context of other pattern mining challenges, such as sequence mining and graph mining.

Data mining identifies patterns in vast databases to support practical decision-making. Using association rule mining, the patterns in the transaction database are found and reveal client behavior. Frequent Itemset Mining (FIM) Finds Often-Buyd Items. FIM's disdain for item significance is a drawback. Practical applications depend on the relevance of the item. Thus, the High-Utility Itemset Mining (HUIM) challenge requires identifying the most profitable items in the transaction database. High-Utility Items (HUI) in the transaction database can be identified using several methods. HUIM methods based on utility lists are novel and outperform traditional methods in terms of memory consumption and execution time. The main limitation of this algorithm is the costly joins of the utility list. This research presents a highly effective swarm intelligence-based method for optimizing HUIM issues. The execution time of the suggested method is assessed. In addition, it is compared to advanced current methods. Testing on publicly available benchmark data sets shows that the swarm-based strategy outperforms current methods. This technique has wide applications in retail analytics, healthcare, fraud detection, finance, supply chain management, and recommendation systems, enabling businesses and researchers to optimize decision making and extract meaningful insights from transactional data.

Several propositional satisfiability (SAT) based encodings have been proposed to deal with various data mining problems including itemset and sequence mining problems. This research issue allows to model data mining problems in a declarative way, while exploiting efficient SAT solving techniques. In this paper, we overview our contributions on the application of propositional satisfiability (SAT) to model and solve itemset mining tasks. We first present a SAT based encoding of frequent closed itemset mining task as a propositional formula whose models corresponds to the patterns to be mined. Secondly, we show that some data mining constraints can be avoided by reformulation. We illustrate this issue by reformulating the closeness constraint using the notion of minimal models. Finally, we also addressed the scalability issue, one of the most important challenge of these nice declarative framework. To this end, we proposed a complete partition based approach whose aim is to avoid encoding the whole database as a single formula. Using a partition on the set of items, our new approach leads to several propositional formulas of reasonable size. The experimental evaluation on several known datasets shows huge improvements in comparison to the direct approach without partitioning, while reducing significantly the performance gap with respect to specialized algorithms.

SufRec, an algorithm for mining association rules: Recursivity and task parallelism

In this internet world, most of the transactions are done in online especially in ecommerce field which creates enormous size of transactional database that grows dynamically at each second. Really it is tough job to analyses the growing transactional database for the betterment of ecommerce business growth. Determining the Interesting pattern form incremental transactional database is evolving research area in the field of Data mining. Frequent Itemset Mining (FIM) approach is followed in earlier days to find the frequent pattern from transactional database. FIM has significant drawback of omitting the interesting factor about each items, such as quantity, price, profit and etc. This drawback is addressed in High Utility Itemset Mining (HUIM) which considers interesting factors of each item. This paper focuses on reviewing the existing state of art algorithms to create a path for the future research in the area of high utility itemset mining.

Mining frequent itemsets from transactional datasets is a well known problem. Thus, various methods have been studied to deal with this issue. Recently, original proposals have emerged from the cross-fertilization between data mining and artificial intelligence. In these declarative approaches, the itemset mining problem is modeled either as a constraint network or a propositional formula whose models correspond to the patterns of interest. In this paper, we focus on the propositional satisfiability based itemset mining framework. Our main goal is to enhance the efficiency of SAT model enumeration algorithms. This issue is particularly crucial for the scalability and competitiveness of such declarative itemset mining approaches. In this context, we deeply analyse the effect of the different SAT solver components on the efficiency of the model enumeration problem. Our analysis includes the main components of modern SAT solvers such as restarts, activity based variable ordering heuristics and clauses learning mechanism. Through extensive experiments, we show that these classical components play an essential role in such procedure to improve the performance by pushing forward the efficiency of SAT solvers. More precisely, our experimental evaluation includes a comparative study in enumerating all the models corresponding to the closed frequent itemsets. Additionally, our experimental analysis is extended to include the Top-k itemset mining problem.

Since the era of data explosion, data mining in transactional databases has become more and more important. There are many data mining techniques like association rule mining, the most important and well-researched one. Furthermore, frequent itemset mining is one of the fundamental but time-consuming steps in association rule mining. Most of the algorithms used in literature find frequent itemsets on search space items having at least a minsup and are not reused for mining next time. To deal with this problem, NOV-FI algorithms are proposed as a new approach in order to quickly detect frequent itemsets from transactional databases using an array of co-occurrences and occurrences of kernel item in at least one transaction. NOV-FI algorithms are easily expanded in distributed systems. Finally, the experimental results show that the proposed algorithms perform better than other existing algorithms.

Probabilistic frequent itemset mining in uncertain transaction databases semantically and computationally differs from traditional techniques applied to standard certain transaction databases. The consideration of existential uncertainty of item(sets), indicating the probability that an item(set) occurs in a transaction, makes traditional techniques inapplicable. In this paper, we introduce new probabilistic formulations of frequent itemsets based on possible world semantics. In this probabilistic context, an itemset X is called frequent if the probability that X occurs in at least minSup transactions is above a given threshold τ. To the best of our knowledge, this is the first approach addressing this problem under possible worlds semantics. In consideration of the probabilistic formulations, we present a framework which is able to solve the Probabilistic Frequent Itemset Mining (PFIM) problem efficiently. An extensive experimental evaluation investigates the impact of our proposed techniques and shows that our approach is orders of magnitude faster than straight-forward approaches.

In modern era, due to the broad applications of uncertain data, mining itemsets over uncertain databases has paying much more attention.Association Rule Mining (ARM) is a well known and most popular technique of Data Mining.It identifies itemsets from the dataset which appears frequently and generates association rules.This is the procedure which is followed by the traditional ARM it does not consider the utility of an itemsets.In real-world applications such as retail marketing, medical diagnosis, client segmentation etc., utility of itemsets is varied on various constraints such as based on cost, profit or revenue.Utility Mining intend to discover itemsets with their utilities by considering profit, quantity, cost or other user preferences.[22]High-utility itemset mining (HUIM) has thus emerged as an important research topic in data mining.But most HUIM algorithms only handle precise data, even though big data collected in reallife applications using experimental measurements or noisy sensors is often uncertain.High-Utility Rare Itemset (HURI) mining finds itemsets from a database which have their utility no less than a given minimum utility threshold and have their support less than a given frequency threshold.Identifying high-utility rare itemsets from a database can help in better business decision making by highlighting the rare itemsets which give high profits so that they can be marketed more to earn good profit.Koh and Rountree (2005) proposed a modified apriori inverse algorithm to generate rare itemsets of user interest.In this paper we propose an efficient algorithm named Mining High Utility Rare Itemsts over Uncertain Database (HURIU) .This novel approach uses the concept of apriori inverse over uncertain databases.This paper will also give the new version or extension of the algorithm HURI proposed by Jyothi et al.The implementation of an algorithm for the analysis is done on JDK 6.1 and referred the sample dataset presented by Lan Y.et al,2015[15] for uncertain database.

Sequential pattern mining has been introduced by Agrawal and Srikant (in: Proceedings of ICDE’95, pp 3–14, 1995) 2 decades ago, and its usefulness has been widely proved for different mining tasks and application fields such as web usage mining, text mining, bioinformatics, fraud detection and so on. Since 1995, despite numerous optimization proposals, sequential pattern mining remains a costly task that often generates too many patterns. This limit, also reached by itemset mining, was circumvented by pattern sampling. Pattern sampling is a non-exhaustive method for instantly discovering relevant patterns that ensures a good interactivity while providing strong statistical guarantees due to its random nature. Curiously, such an approach investigated for different kinds of patterns including itemsets and subgraphs has not yet been applied to sequential patterns. In this paper, we propose the first method dedicated to sequential pattern sampling. In addition to address sequential data, the originality of our approach is to introduce a class of interestingness measures relying on the norm of the sequence, named norm-based utilities. In particular, it enables to add constraints on the norm of sampled patterns to control the length of the drawn patterns and to avoid the pitfall of the “long tail” where the rarest patterns flood the user. We propose a new two-step random procedure integrating this class of measures, named $${\textsc {NUSSampling}}$$ that randomly draws sequential patterns according to frequency weighted by a norm-based utility. We demonstrate that this method performs an exact sampling according to the underlying measure. Moreover, despite the use of rejection sampling, the experimental study shows that $${\textsc {NUSSampling}}$$ remains efficient. We especially focus on the interest of norm constraints and exponential decays that help to draw general patterns of the “head”. We also illustrate how to benefit from these sampled patterns to instantly build an associative classifier dedicated to sequences. This classification approach rivals state-of-the-art proposals showing the interest of sequential pattern sampling with norm-based utility.

In this chapter, we present granule based inter association mining to reduce the complexity of intertransaction association rule mining. To compare with other methods, our method can reduce the width of sliding windows. It uses granules to replace extended item sets. Thus, we do not need to consider too many combinations of extended items. We also propose the concept of precision in order to evaluate the effectiveness of intertransaction association rule mining. The experiments show that the proposed method is promising.

Frequent itemsets (FIs) mining from uncertain database is a very popular research area nowadays. Many algorithms have been proposed to mine FI from uncertain database. But in typical FI mining process, all the FIs have to be mined individually, which needs a huge memory. Four trees are proposed in this paper which are: (i) maximal frequent itemset from uncertain database (MFU) tree which contains only the maximal frequent itemsets generated from uncertain database, (ii) closed frequent itemset from uncertain database (CFU) tree which contains only closed frequent itemsets generated from uncertain database, (iii) maximal frequent itemset from uncertain data stream (MFUS) tree which contains maximal frequent itemsets generated from uncertain data stream and (iv) closed frequent itemset from uncertain data stream (CFUS) tree which contains closed frequent itemsets generated from uncertain data stream. Experimental results are also presented which show that maximal and closed frequent itemsets mining requires less time and memory than typical frequent itemsets mining.

Mining high-utility itemsets based on particle swarm optimization