Efficient Data Mining Algorithms for Mining Frequent/Closed/Maximal Itemsets

Provided by: International Journal of Advanced Research in Computer Science and Software Engineering (IJARCSSE)
Topic: Data Management
Format: PDF
Data mining algorithms have been around for discovering knowledge from large real world data sets. Especially they operate on historical data in OLAP (On-Line Analytical Processing) applications. Frequent itemset mining is used in many applications such as query expansion, inductive databases, and association rule mining. When an itemset is repeated in specified number of times in given dataset, it is known as frequent itemset. Frequent itemset which does not appear in other frequent itemset is known as maximal itemset. If the frequent itemset is not included in other itemset then it is known as closed itemset. These itemsets have their utility in data mining applications. Recently Uno et al. proposed algorithms to discover frequent itemsets, closed itemsets and maximal itemsets.

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