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

• Main Authors: S Shankar, T Purusothaman
• Format: Article
• Language: English
• Published: Ubiquity Press 2010-02-01
• Series: Data Science Journal
• Subjects: Data Mining; Frequent Patterns; Association Rules; FP-Growth; Economic Utility; Weight; Significance; Interestingness; Subjective Interestingness
• Online Access: http://datascience.codata.org/articles/124

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.