A Survey on Efficient Incremental Algorithm for Mining High Utility Itemsets in Distributed and Dynamic Database

Data Mining is the process of analyzing data from different perspectives and summarizing it into useful information. It can be defined as the activity that extracts information contained in very large database. That information can be used to increase the revenue or cut costs. 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 to combine the High Utility Pattern Mining and Incremental Frequent Pattern Mining. The traditional method of mining frequent itemset assumes that the data is centralized and static, which impose excessive communication overhead when the data is distributed, and they waste computational 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 examining the entire database, and has minimal 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 remove the infrequent itemset from a utility pattern tree structure. Hence, it provides faster execution, that is reduced time and cost. Keywords— Data Mining, Association Rule Mining, High Utility Mining, Incremental Mining.