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Research Papers

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

Authors:

S Shankar ,

Department of Information Technology, Sri Krishna College of Engineering and Technology, Coimbatore,Tamilnadu, India., IN
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T Purusothaman

Department of Computer Science and Engineering, Government College of Technology, Coimbatore, Tamilnadu, India.
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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: http://doi.org/10.2481/dsj.008-030
How to Cite: Shankar, S. & Purusothaman, T., (2010). Discovering Imperceptible Associations Based on Interestingness: A Utility-Oriented Data Mining. Data Science Journal. 9, pp.1–12. DOI: http://doi.org/10.2481/dsj.008-030
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Published on 12 Feb 2010.
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