Knowledge Discovery in Medical Data by using Rough Set Rule Induction Algorithms
作者： P. K. Srimani , Manjula Sanjay Koti
DOI: 10.17485/IJST/2014/V7I7/44729
关键词: Data mining 、 Context (language use) 、 Knowledge extraction 、 Algorithm 、 Machine learning 、 Dominance-based rough set approach 、 Rough set 、 Raw data 、 Rule induction 、 Brute-force search 、 Mathematics 、 Decision rule 、 Artificial intelligence
摘要: In the performance of data mining and knowledge discovery activities, rough set theory has been regarded as a powerful, feasible effective methodology. There is need for analysis medical that deals with incomplete inconsistent information tremendous manipulation at different levels. this context, rule induction algorithms are capable generating decision rules which can potentially provide new insight profound knowledge. By taking into consideration all above aspects, present investigation carried out. The results clearly show approach certainly useful tool applications. Relationships patterns within could genetic offer an attractive solving feature subset selection problem. process finding or meaning in raw called databases. used study are: Exhaustive search, Covering, LEM2 Genetic algorithms. Rules generated improved case mentioned four Further, by applying shortening ratio 0.8. Some important include maximum coverage 100% observed exhaustive
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