IHAC: Incorporating Heuristics for Efficient Rule Generation & Rule Selection in Associative Classification

Parashu Ram Pal (), Pankaj Pathak () and Shkurte Luma-Osmani ()
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Parashu Ram Pal: ABES Engineering College, Ghaziabad, Uttar Pradesh, India
Pankaj Pathak: Symbiosis Institute of Digital and Telecom Management, Symbiosis International (Deemed University), Pune, India
Shkurte Luma-Osmani: University of Tetova, North Macedonia

Journal of Information & Knowledge Management (JIKM), 2021, vol. 20, issue 01, 1-13

Abstract: Associations rule mining along with classification rule mining are both significant techniques of mining of knowledge in the area of knowledge discovery in massive databases stored in different geographic locations of the world. Based on such combination of these two, class association rules for mining or associative classification methods have been generated, which, in far too many cases, showed higher prediction accuracy than platitudinous conventional classifiers. Motivated by the study, in this paper, we proposed a new approach, namely IHAC (Incorporating Heuristics for efficient rule generation & rule selection in Associative Classification). First, it utilises the database to decrease the search space and then explicitly explores the potent class association rules from the optimised database. This also blends rule generation and classifier building to speed up the overall classifier construction cycle. Experimental findings showed that IHAC performs better than any further associative classification methods.

Keywords: Knowledge discovery; data mining; associative classification; IHAC; rule mining (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1142/S0219649221500106

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