CoverSize: A Global Constraint for Frequency-Based Itemset Mining

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.

Itemset mining is an important subfield of data mining, which consists of discovering interesting and useful patterns in transaction databases. The traditional task of frequent itemset mining is to discover groups of items (itemsets) that appear frequently together in transactions made by customers. Although itemset mining was designed for market basket analysis, it can be viewed more generally as the task of discovering groups of attribute values frequently cooccurring in databases. Because of its numerous applications in domains such as bioinformatics, text mining, product recommendation, e‐learning, and web click stream analysis, itemset mining has become a popular research area. This study provides an up‐to‐date survey that can serve both as an introduction and as a guide to recent advances and opportunities in the field. The problem of frequent itemset mining and its applications are described. Moreover, main approaches and strategies to solve itemset mining problems are presented, as well as their characteristics are provided. Limitations of traditional frequent itemset mining approaches are also highlighted, and extensions of the task of itemset mining are presented such as high‐utility itemset mining, rare itemset mining, fuzzy itemset mining, and uncertain itemset mining. This study also discusses research opportunities and the relationship to other popular pattern mining problems, such as sequential pattern mining, episode mining, subgraph mining, and association rule mining. Main open‐source libraries of itemset mining implementations are also briefly presented.WIREs Data Mining Knowl Discov2017, 7:e1207. doi: 10.1002/widm.1207This article is categorized under:Algorithmic Development > Association RulesTechnologies > Association Rules

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

Online mining of data streams is an important data mining problem with broad applications. However, it is also a difficult problem since the streaming data possess some inherent characteristics. In this paper, we propose a new single-pass algorithm, called DSM-FI (data stream mining for frequent itemsets), for online incremental mining of frequent itemsets over a continuous stream of online transactions. According to the proposed algorithm, each transaction of the stream is projected into a set of sub-transactions, and these sub-transactions are inserted into a new in-memory summary data structure, called SFI-forest (summary frequent itemset forest) for maintaining the set of all frequent itemsets embedded in the transaction data stream generated so far. Finally, the set of all frequent itemsets is determined from the current SFI-forest. Theoretical analysis and experimental studies show that the proposed DSM-FI algorithm uses stable memory, makes only one pass over an online transactional data stream, and outperforms the existing algorithms of one-pass mining of frequent itemsets.

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.

Efficient and accurate personalized product recommendations through frequent item set mining fusion algorithm

As technology advances, floods of data can be produced and shared in many applications such as wireless sensor networks or Web click streams. This calls for efficient mining techniques for extracting useful information and knowledge from streams of data. In this paper, we propose a novel algorithm for stream mining of frequent itemsets in a limited memory environment. This algorithm uses a compact tree structure to capture important contents from streams of data. By exploiting its nice properties, such a tree structure can be easily maintained and can be used for mining frequent itemsets, as well as other patterns like constrained itemsets, even when the available memory space is small.

Frequent itemset mining is a popular and important first step in the analysis of data arising in a broad range of applications. The traditional “exact” model for frequent itemsets requires that every item occur in each supporting transaction. However, real data is typically subject to noise and measurement error. To date, the effect of noise on exact frequent pattern mining algorithms have been addressed primarily through simulation studies, and there has been limited attention to the development of noise tolerant algorithms. In this paper we propose a noise tolerant itemset model, which we call approximate frequent itemsets (AFI). Like frequent itemsets, the AFI model requires that an itemset has a minimum number of supporting transactions. However, the AFI model tolerates a controlled fraction of errors in each item and each supporting transaction. Motivating this model are theoretical results (and a supporting simulation study presented here) which state that, in the presence of even low levels of noise, large frequent itemsets are broken into fragments of logarithmic size; thus the itemsets cannot be recovered by a routine application of frequent itemset mining. By contrast, we provide theoretical results showing that the AFI criterion is well suited to recovery of block structures subject to noise. We developed and implemented an algorithm to mine AFIs that generalizes the level-wise enumeration of frequent itemsets by allowing noise. We propose the noise-tolerant support threshold, a relaxed version of support, which varies with the length of the itemset and the noise threshold. We exhibit an Apriori property that permits the pruning of an itemset if any of its sub-itemset is not sufficiently supported. Several experiments presented demonstrate that the AFI algorithm enables better recoverability of frequent patterns under noisy conditions than existing frequent itemset mining approaches. Noise-tolerant support pruning also renders an order of magnitude performance gain over existing methods.

Frequently used items mining is a significant subject of data mining studies. In the last ten years, due to innovative development, the quantity of data has grown exponentially. For frequent Itemset (FIs) mining applications, it imposes new challenges. Misconceived information may be found in recent algorithms, including both threshold and size based algorithms. Threshold value plays a central role in generating frequent itemsets from the given dataset. Selecting a support threshold value is very complicated for those unaware of the dataset’s characteristics. The performance of algorithms for finding FIs without the support threshold is, however, deficient due to heavy computation. Therefore, we have proposed a method to discover FIs without the support threshold, called Top-k frequent itemsets mining (TKFIM). It uses class equivalence and set-theory concepts for mining FIs. The proposed procedure does not miss any FIs; thus, accurate frequent patterns are mined. Furthermore, the results are compared with state-of-the-art techniques such as Top-k miner and Build Once and Mine Once (BOMO). It is found that the proposed TKFIM has outperformed the results of these approaches in terms of execution and performance, achieving 92.70, 35.87, 28.53, and 81.27 percent gain on Top-k miner using Chess, Mushroom, and Connect and T1014D100K datasets, respectively. Similarly, it has achieved a performance gain of 97.14, 100, 78.10, 99.70 percent on BOMO using Chess, Mushroom, Connect, and T1014D100K datasets, respectively. Therefore, it is argued that the proposed procedure may be adopted on a large dataset for better performance.

Vertical itemset mining is an important frequent pattern mining problem with broad applications. It is challenging since one may need to examine a combinatorial explosive number of possible patterns of items of a dataset in a traditional horizontal algorithm. Since high dimensional datasets typically contain a large number of columns and a small number of rows, vertical itemset mining algorithms, which extract the frequent itemsets of dataset by producing all combination of rows ids, are a good alternative for horizontal algorithms in mining frequent itemsets from high dimensional dataset. Since a rowset can be simply produced from its subsets by adding a new row id to a sub rowset, many bottom up vertical itemset mining algorithms are designed and represented in the literature. However, bottom up vertical mining algorithms suffer from a main drawback. Bottom-up algorithms start the process of generating and testing of rowsets from the small rowsets and go on to the larger rowsets, whereas the small rowsets cannot produce a frequent itemsets because they contain less than minimum support threshold number of rows. In this paper, we described a new efficient vertical top down algorithm called VTD (Vertical Top Down) to conduct mining of frequent itemsets in high dimensional datasets. Our top down approach employed the minimum support threshold to prune the rowsets which any itemset could not be extracted from them. Several experiments on real bioinformatics datasets showed that VTD is orders of magnitude better than previous closed pattern mining algorithms. Our performance study showed that this algorithm outperformed substantially the best former algorithms.

Frequent itemsets mining is one of the interesting applications of data mining. Recently data mining has got a great deal of attention due to the explosive growth in data and the economic and scientific need for turning such data into useful information. However, the traditional frequent itemsets mining algorithms have become inefficient to work with large datasets effectively on a single machine due to computational power and memory limits. Current methods prefer to control the execution time and output by using higher minimum support thresholds, which lead to less candidates and frequent itemsets. In this paper, an improved-version of Apriori like HFDM-EB algorithm that can deal with lower minimum support thresholds is proposed for mining frequent itemsets over big transactional data on Hadoop framework and by utilizing compressed bitmaps. The experimental results show that the improved algorithm is efficient and scalable for mining frequent itemsets in big data.

Mining frequent itemsets can often generate a large number of frequent itemsets. Recent studies proposed mining itemset with the different types of constraint. The paper is to mine frequent itemsets, where a one: does not contain any item of C0 or contains at least one item of C0. The set of all those ones is partitioned into equivalence classes. Without loss of generality, we only investigate each class independently. One class is represented by a frequent closed set L and splits into two disjoint sub-classes. The first contains frequent itemsets that do not contain any item of C0. It is generated from the corresponding generators. The second includes in two subsets of the frequent itemsets coming from the generators containing in C0, and the ones obtained by connecting each non-empty subset of L(C0 with each element of the first.

Frequent pattern mining is the most important phase of association rule mining process because of its time and space complexity. Several methods have attempted to improve the performance of association rule mining by enhancing frequent pattern mining efficiency. Due to the large size of the data-sets and huge amounts of data which should be mined, many parallel and distributed mining approaches have been introduced to divide data-sets or to distribute mining processes between multiple processors or computers and thus, improve the efficiency of the mining process. In this paper, we propose a hadoop-based parallel implementation of PrePost+ algorithm for frequent itemset mining. In our parallel approach, the process of constructing N-Lists of itemsets has been distributed between the mappers and the operation of the final pruning process and extracting frequent itemsets has been carried out by reducers in a map-reduce parallel programming model. The experimental results show that our hadoop-based PrePost+(HBPrePost+) algorithm outperforms one of the best existing parallel methods of frequent itemset mining (PARMA) in terms of execution time.

Graph-based biomedical text summarization: An itemset mining and sentence clustering approach

Finding frequent itemsets is a popular data mining problem, aiming to extract hidden patterns from a transactional database. Several bio-inspired approaches to solve this problem have been proposed to overcome the poor performance of exact algorithms, such as Apriori and FPGrowth. Approaches based on genetic algorithms are among the most efficient ones from the point of view of runtime performance, but they are still inefficient in terms of solution’s quality, i.e., the number of frequent itemsets discovered. To deal with this issue, we propose in this paper a new genetic algorithm for finding frequent itemsets called GA-Apriori, in which the crossover and mutation operators are defined by taking into account the Apriori heuristic principle. The results of our evaluation show that GA-Apriori outperforms other approaches to frequent itemset mining based on genetic algorithms, especially when dealing with large instances. The experiments also show that GA-Apriori is competitive with exact approaches in terms of the number of frequent itemsets discovered.