Title: An improved incremental algorithm for mining weighted class-association rules

Authors: B. Subbulakshmi; C. Deisy

Addresses: Department of Computer Science and Engineering, Thiagarajar College of Engineering, Madurai, India ' Department of Computer Science and Engineering, Thiagarajar College of Engineering, Madurai, India

Abstract: Constructing fast and accurate classifiers for large data sets is an important task in data mining. Associative classification can produce more efficient and accurate classifiers than traditional classification techniques. Weighted class association rule (WCAR) mining reflects significance of items by considering their weight. Moreover, real time databases are dynamic. This influences the need for incremental approach for classification. Existing incremental classification algorithms suffer from issues like longer execution time and higher memory usage. This paper proposes an algorithm which uses hash structure to store weighted frequent items and the concept of difference of object identifiers to compute the support faster. For mining incremental databases, pre-large concept is used to reduce the number of re-scans over the original database. The proposed algorithm was implemented and tested on experimental data sets taken from UCI repository. The results show that proposed algorithm for mining WCARs gives better results compared to existing algorithm.

Keywords: class association rules; CARs; weighted frequent itemsets; WFI; incremental mining; hash structure; weighted class association rules; associative classifier.

DOI: 10.1504/IJBIDM.2018.088437

International Journal of Business Intelligence and Data Mining, 2018 Vol.13 No.1/2/3, pp.291 - 308

Received: 06 Oct 2016
Accepted: 08 Jan 2017
Published online: 07 Dec 2017 *

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