Kok Meng Hoe

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In this paper we present a strategy together with its computational implementation to intelligently analyze data clusters in terms of symbolic cluster-defining rules. We present a symbolic rule extraction workbench that leverages rough set theory to inductively extract CNF form symbolic rules from un-annotated continuous-valued data-vectors. Our workbench(More)
The self-organization behavior exhibited by ants may be modeled to solve real world clustering problems. The general idea of artificial ants walking around in search space to pick up, or drop an item based upon some probability measure has been examined to cluster a large number of World Wide Web (WWW) documents. However, this idea is extended with the(More)
The application of data mining techniques upon medical data is certainly beneficial for researchers interested in discerning the complexity of healthcare processes in real-life operational situations. In this paper we present a methodology, together with its computational implementation, for the automated extraction of data-defining CNF symbolic rules from(More)
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