Discovering Imperceptible Associations Based on Interestingness: A Utility-Oriented Data Mining

Abstract

This article proposes an innovative utility sentient approach for the mining of interesting association patterns from transaction databases. First, frequent patterns are discovered from the transaction database using the FP-Growth algorithm. From the frequent patterns mined, this approach extracts novel interesting association patterns with emphasis on significance, utility, and the subjective interests of the users. The experimental results portray the efficiency of this approach in mining utility-oriented and interesting association rules. A comparative analysis is also presented to illustrate our approach's effectiveness.