An evolutionary approach to discover intra-and inter-class exceptions in databases

Authors:
Jyoti Vashishtha;Dharminder Kumar;Saroj Ratnoo
Affiliations:
Department of Computer Science & Engineering, Guru Jambheshwar University of Science & Technology, Hisar, Haryana, India;Department of Computer Science & Engineering, Guru Jambheshwar University of Science & Technology, Hisar, Haryana, India;Department of Computer Science & Engineering, Guru Jambheshwar University of Science & Technology, Hisar, Haryana, India
Venue:
International Journal of Intelligent Systems Technologies and Applications
Year:
2013

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Abstract

Data mining algorithms produce information of a statistical nature that contains accurate and reliable knowledge. However, in many cases these algorithms do not discover hidden facts which may be interesting to users. Therefore, the recent aim of knowledge discovery in databases KDD is to expose patterns that are exceptions to the existing knowledge. Exceptions are considered interesting as they add extraordinary facts to the knowledge base and create an incentive in users to re-examine their decisions. Discovering exceptions along with the decision rules increases the quality of decision making in those rare circumstances where the rules are not applicable. This paper is an attempt to devise a framework to discover exceptions using an evolutionary approach. In this work, we have categorised exceptions as inter and intra class. Experimental results are presented to demonstrate the performance of the proposed algorithm.