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Determination of interesting rules in FCA using information gain
Published in IEEE
Pages: 304 - 308
The difficult job in association rules is to identify the frequent item sets immersed into the huge collection of data. The association rules can be discovered using Formal Concept Analysis (FCA). Several contexts often contain large number of rules and hence interesting rules are required to be determined. With this objective, this paper proposes a method for determining interesting rules in FCA involving many-valued contexts based on Shannon's information entropy (IE) theory. For this purpose we define a gain-lift measure on association rules. The proposed method is illustrated by means of an example available from the field of medical diagnosis. © 2014 IEEE.
About the journal
JournalData powered by Typeset2014 First International Conference on Networks & Soft Computing (ICNSC2014)
PublisherData powered by TypesetIEEE
Open Access0