Online mining of frequent itemsets over a stream sliding window is one of the most important problems in stream data mining with broad applications. It is also a difficult issue since the streaming data possess some challenging characteristics, such as unknown or unbound size, possibly a very fast arrival rate, inability to backtrack over previously arrived transactions, and a lack of system co...

We present a new approach based on Genetic Algorithm to generate maximal frequent itemsets from large databases. This new algorithm called GeneticMax is heuristic which mimics natural selection approaches to finding maximal frequent itemsets in an efficient way. The search strategy of this algorithm uses lexicographic tree that avoids level by level searching, which finally reduces the time req...

In mining frequent itemsets, one of most important algorithm is FP-growth. FP-growth proposes an algorithm to compress information needed for mining frequent itemsets in FP-tree and recursively constructs FP-trees to find all frequent itemsets. In this paper, we propose the EFP-growth (enhanced FPgrowth) algorithm to achieve the quality of FP-growth. Our proposed method implemented the EFPGrowt...

ÐAssociation rule discovery has emerged as an important problem in knowledge discovery and data mining. The association mining task consists of identifying the frequent itemsets and then, forming conditional implication rules among them. In this paper, we present efficient algorithms for the discovery of frequent itemsets which forms the compute intensive phase of the task. The algorithms utili...

Frequent Itemsets (FIs) mining is a fundamental primitive in data mining that requires to identify all itemsets appearing in a fraction at least θ of a transactional dataset D. Often though, the ultimate goal of mining D is not an analysis of the dataset per se, but the understanding of the underlying process that generated D. Specifically, in many applications D is a collection of samples obta...

We present GenMax, a backtrack search based algorithm for mining maximal frequent itemsets. GenMax uses a number of optimizations to prune the search space. It uses a novel technique called progressive focusing to perform maximality checking, and diffset propagation to perform fast frequency computation. Systematic experimental comparison with previous work indicates that different methods have...

Frequent itemset mining is a promising approach to the study of genomic profiling data. Here a dataset consists of real numbers describing the relative level in which a clone occurs in human DNA for given patient samples. One can then mine, for example, for sets of samples that share some common behavior on the clones, i.e., gains or losses. Frequent itemsets show promising biological expressiv...

This paper presents an efficient One Pass Association Mining technique i.e. OPAM, which finds all the frequent itemsets without generating any candidate sets. OPAM is basically an integration of two techniques: a correlogram matrix based technique to generate all the frequent 1and 2-itemset in a single scan over the database and a technique that uses vertical layout concept to generate the rest...

The mining of frequent itemsets is a basic and essential work in many data mining applications. Frequent itemsets extraction with frequent pattern and rules boosts the applications like Association rule mining, co-relations also in product sale and marketing. In extraction process of frequent itemsets there are number of algorithms used Like FP-growth,E-clat etc. But unfortunately these algorit...

High utility itemsets mining extends frequent pattern mining to discover itemsets in a transaction database with utility values above a given threshold. However, mining high utility itemsets presents a greater challenge than frequent itemset mining, since high utility itemsets lack the anti-monotone property of frequent itemsets. Transaction Weighted Utility (TWU) proposed recently by researche...