Raj P. Gopalan

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Frequent pattern mining discovers patterns in transaction databases based only on the relative frequency of occurrence of items without considering their utility. For many real world applications, however, utility of itemsets based on cost, profit or revenue is of importance. The utility mining problem is to find itemsets that have higher utility than a(More)
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(More)
Mining High Utility Itemsets from a transaction database is to find itemsests that have utility above a user-specified threshold. This problem is an extension of Frequent Itemset Mining, which discovers itemsets that occur frequently (i.e. with occurrence count larger than a user given value). The problem of finding High Utility Itemsets is challenging,(More)
Frequent itemset mining (FIM) is an essential part of association rules mining. Its application for other data mining tasks has also been recognized. It has been an active research area and a large number of algorithms have been developed. In this paper, we propose another pattern growth algorithm which uses a more compact data structure named Compressed(More)
Efficient mining of frequent patterns from large databases has been an active area of research since it is the most expensive step in association rules mining. In this paper, we present an algorithm for finding complete frequent patterns from very large dense datasets in a cluster environment. The data needs to be distributed to the nodes of the cluster(More)
Discovering association rules that identify relationships among sets of items is an important problem in data mining. Finding frequent item sets is computationally the most expensive step in association rule discovery and therefore it has attracted significant research attention. In this paper, we present a more efficient algorithm for mining complete sets(More)