A classification of methods for frequent pattern mining
Journal Title: IOSR Journals (IOSR Journal of Computer Engineering) - Year 2015, Vol 17, Issue 1
Abstract
Abstract: Data mining refers to extracting knowledge from large amounts of data. Frequent pattern mining is aheavily researched area in the field of data mining with wide range of applications. Frequent itemsets is one ofthe emerging task in data mining. A many algorithms has been proposed to determine frequent patterns. Apriorialgorithm is the first algorithm proposed in this field. An Apriori algorithm having two major limitation firstgenerate huge candidate itemsets and second more times scan the database. Problem, to be solved somemethods for frequent itemset mining in the paper. Three major factors used in frequent itemset mining such astime, scalability, efficiency. In this paper we have analyze various algorithm for frequent itemset mining suchas CBT-fi, Index-BitTableFI, Hierarchical Partitioning, Matrix based Data Structure, Bitwise AND, Two-FoldCross-Validation and binary based Semi-Apriori Algorithm also discuss advantages & disadvantages of the frequent itemset mining algorithm.
Authors and Affiliations
Patel Atul R. , Patel Tushar S.
Keywords
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