Research On Frequent Closed Pattern Mining Algorithm Based On COFI-Tree

Efficient discovery of frequent patterns from large databases is an active research area in data mining with broad applications in industry and deep implications in many areas of data mining. Although many efficient frequent-pattern mining techniques have been developed in the last decade, most of them assume relatively small databases, leaving extremely large but realistic datasets out of reach. Given different small datasets with different characteristics, it is difficult to say which approach would be a winner. Moreover, on the same dataset with different support thresholds different winners could be proclaimed. Difference in performance becomes clear only when dealing with very large datasets. However, when a transaction database contains large number of large frequent itemsets, mining all frequent itemsets might not be a good idea. For example, if there is a frequent itemset with size L, then all 2~L-1 nonempty subsets of the itemset have to be generated. So the current research focuses on some efficient mining algorithms to mining large database like WalMark, UPS, etc.