Discovering Imperceptible Associations Based on Interestingness: A Utility-Oriented Data Mining
By: S Shankar and T Purusothaman
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.
DOI: https://doi.org/10.2481/dsj.008-030 | Journal eISSN: 1683-1470
Language: English
Page range: 1 - 12
Published on: Feb 12, 2010
Published by: Ubiquity Press
In partnership with: Paradigm Publishing Services
Publication frequency: 1 issue per year
Keywords:
© 2010 S Shankar, T Purusothaman, published by Ubiquity Press
This work is licensed under the Creative Commons Attribution 4.0 License.