A New Approach to Evaluate Best-k High Beneficial Itemsets

Abstract

The main purpose of this work is to develop a superior structure to Evaluate  Best-k high beneficial itemsets. Here K is the picked portion of high beneficial itemsets that is to be established. High beneficial item set tunneling is surely a prominent study in data mining but the factors for setting minimum utility margin is definitely a difficult task. In this work, a novel approach to evolve Best-k high beneficial itemsets named Enhanced Best-k high beneficial item set tunneling (EBKU) is proposed. EBKU uses B+ Tree data structure instead of using a Utility Pattern Tree (UP-Tree) data structure that is used in existing Best-k high beneficial itemsets tunneling (TKU) method. Although TKU helps to reduce the time taken for the process of tunneling by reducing the total number of database scans to two, the complexity lies in the UP-Tree traversal for obtaining potential Best-k high beneficial itemsets. B+ Tree used in EBKU does not have data associated with interior nodes so that more keys can fit into the memory. The leaf nodes of B+ Tree are linearly linked, so a full scan of a tree requires only one linear pass through all the leaf nodes.