Implementation of Efficient Algorithm for Mining High Utility Itemsets in Distributed and Dynamic Database

Association Rule Mining (ARM) is finding out the frequent itemsets or patterns among the existing items from the given database. High Utility Pattern Mining has become the recent research with respect to data mining. The proposed work is High Utility Pattern for distributed and dynamic database. The traditional method of mining frequent itemset mining embrace that the data is astride and sedentary, which impose extreme communication overhead when the data is distributed, and they waste calculation resources when the data is dynamic. To overcome this, Utility Pattern Mining Algorithm is proposed, in which itemsets are maintained in a tree based data structure, called as Utility Pattern Tree, and it generates the itemset without stare the entire database, and has sparse communication overhead when mining with respect to distributed and dynamic databases. A quick update incremental algorithm is used which scans only the incremental database as well as collects only the support count of newly generated frequent itemsets. Incremental Mining Algorithm not only includes new itemset into a tree but also discard the infrequent itemset from a utility pattern tree structure. Hence it provides faster execution, minimal communication and cost when compared to the existing methods. KeywordsAssociation Rule Mining, High Utility Pattern Mining, Distributed and Dynamic Database, Incremental pattern Mining.