Research Article Open Access

SELECTING PERFECT INTERESTINGNESS MEASURES BY COEFFICIENT OF VARIATION BASED RANKING ALGORITHM

K. Selvarangam1 and K. Ramesh Kumar1
  • 1 Hindustan University, India

Abstract

Ranking interestingness measure is an active and essential research domain in the process of knowledge discovery from the extracted rules. Since various measures proposed by many researchers in various situations increases the list of measures and these are not able to use as a common measures to evaluate the rules, knowledge finders are not able to identify a perfect measure to ensure the actual knowledge on database. In this study, we presented about a ranking method to identify a perfect measure, which also reduces the number of measures. Ranking will be done by increasing order of Coefficient of Variation (CV) and not applicable measures are eliminated. Also we introduced heuristic association measures, U cost, S cost, R cost, T combined cost and ranked with existing measures using CV based ranking algorithm, our measures are placed in better position on ranking, compared with the existing measures.

Journal of Computer Science
Volume 10 No. 9, 2014, 1672-1679

DOI: https://doi.org/10.3844/jcssp.2014.1672.1679

Submitted On: 17 February 2014 Published On: 17 April 2014

How to Cite: Selvarangam, K. & Kumar, K. R. (2014). SELECTING PERFECT INTERESTINGNESS MEASURES BY COEFFICIENT OF VARIATION BASED RANKING ALGORITHM. Journal of Computer Science, 10(9), 1672-1679. https://doi.org/10.3844/jcssp.2014.1672.1679

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Keywords

  • Pattern
  • Interestingness Measures
  • Association Rule
  • Coefficient of Variation