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Research Article
Measuring the Interestingness of Classification Rules

Sanjeev Sharma, Swati Khare and Sudhir Sharma

Asian Journal of Information Management, 2007, 1(2), 43-49.


Data mining tools and techniques provide various applications with novel and significant knowledge. This knowledge can be leveraged to gain competitive advantage. However, the automated nature of data mining algorithms may result in a glut of patterns-the sheer numbers of which contribute to incomprehensibility. Importance of automated methods that address this immensity problem, particularly with respect to practical application of data mining results, cannot be overstated. We provide a survey of one important approach, namely interestingness measure and discuss its application to extract interesting results out of large number of rules generated by the classification rule generator program. We have used the US Census database of UCI repository as our experimental domain. Rules are generated by the Christian Borgelts classification rule discovery program. A new rule selection mechanism is introduced and experimental results show that our method is effective in finding interesting rules.

ASCI-ID: 24-7

Cited References Fulltext

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