The discovery of association rules showing conditions of data co-occurrence has attracted the most attention in data mining. An example of an association rule is the rule “the customer who bought bread and butter also bought milk,” expressed by T(bread; butter)→T(milk). Let I ={x1,x2,...,xm} be a set of (data) items, called the domain; let D be a collection of records (transactions), where each...

Given a large collection of transactions containing items, a basic common data mining problem is to extract the so-called frequent itemsets (i.e., set of items appearing in at least a given number of transactions). In this paper, we propose a structure called free-sets, from which we can approximate any itemset support (i.e., the number of transactions containing the itemset) and we formalize t...

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Closed itemset mining is a popular research in data mining. It was proposed to avoid a large number of redundant itemsets in frequent itemset mining. Various algorithms were proposed with efficient strategies to generate closed itemsets. This paper aims to study the existence algorithms used to mine closed itemsets. The various strategies in the algorithms are presented and analyzed in this paper.

A popular approach to explain over-contribution in public good games is based on the assumption that people care (either positively or negatively) about the utility of other participants. Over-contribution then is an outcome of utility maximization where utility depends on subjects’ own payoffs as well as on the payoffs of other players. In this paper, I study to what extent this assumption of ...

Data is growing at an enormous rate and mining this data is becoming a herculean task. Association Rule mining is one of the important algorithms used in data mining and mining frequent itemset is a crucial step in this process which consumes most of the processing time. Parallelizing the algorithm at various levels of computation will not only speed up the process but will also allow it to han...

A new algorithm for minimal infrequent itemset mining is presented. Potential applications of finding infrequent itemsets include statistical disclosure risk assessment, bioinformatics, and fraud detection. This is the first algorithm designed specifically for finding these rare itemsets. Many itemset properties used implicitly in the algorithm are proved. The problem is shown to be NP-complete...