The output of boolean association rule mining algorithms is often too large for manual examination. For dense datasets, it is often impractical to even generate all frequent itemsets. The closed itemset approach handles this information overload by pruning “uninteresting” rules following the observation that most rules can be derived from other rules. In this paper, we propose a new framework, ...

Frequent Itemset Mining is a well-known concept in data sciences. If we feed frequent itemset miner algorithms with large datasets they become resource hungry fast as their search space explodes. This problem is even more apparent when we try to use them on Big Data. Recent advances in parallel programming provides good solutions to deal with large datasets but they present their own problems w...

Updates on an operational database bring forth the challenge of keeping the frequent itemsets up-to-date without re-running the itemset mining algorithms. Studies on dynamic itemset mining, which is the solution to such an update problem, have to address some challenges as handling i) updates without re-running the base algorithm, ii) changes in the support threshold, iii) new items and iv) add...

Itemset mining and graph mining have attracted considerable attention in the field of data mining, since they have many important applications in various areas such as biology, marketing, and social network analysis. However, most existing studies focus only on either itemset mining or graph mining, and only a few studies have addressed a combination of both. In this paper, we introduce a new p...

Mining interesting itemsets is a popular topic in the data mining community. The objective of this problem is to mine all interesting itemsets, with respect to a given interestingness measure. While considerable efforts have being spent on justifying the various interestingness measures, the algorithms that mine them are not quite well-studied, except in the case support, which has resulted in ...

Association rule mining is the process of discovering relationships among the data items in large database. It is one of the most important problems in the field of data mining. Finding frequent itemsets is one of the most computationally expensive tasks in association rule mining. The classical frequent itemset mining approaches mine the frequent itemsets from the database where presence of an...

Association Rule Mining (ARM) is finding out the frequent itemsets or patterns among the existing items from the given database. High Utility Pattern Mining has become the recent research with respect to data mining. The proposed work is High Utility Pattern for distributed and dynamic database. The traditional method of mining frequent itemset mining embrace that the data is astride and sedent...

In recent years, there have been increasing efforts to apply association rule mining to build Associative Classification (AC) models. However, the similar area that applies association rule mining to build Associative Regression (AR) models has not been well explored. In this work, we fill this gap by presenting a novel regression model based on association rules called AREM. AREM derives a set...

Frequent pattern mining is the process of finding a pattern (a set of items, subsequences, substructures, etc.) that occurs frequently in a data set. It was proposed in the context of frequent itemsets and association rule mining. Frequent pattern mining is used to find inherent regularities in data. What products were often purchased together? Its applications include basket data analysis, cro...