SRPF Interest Measure Based Classification to Extract Important Patterns-Reference-Cited by-同舟云学术

SRPF Interest Measure Based Classification to Extract Important Patterns

Published:2018-09-08 Issue: Volume: Page:523-530
ISSN:2367-3370
Container-title:Proceedings of 2nd International Conference on Communication, Computing and Networking
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Author:

Sharma Ochin,Kumar Suresh,Joshi Nisheeth

Publisher

Springer Singapore

Link

http://link.springer.com/content/pdf/10.1007/978-981-13-1217-5_50

Reference22 articles.

1. M. Hahsler, A Probabilistic Comparison of Commonly Used Interest Measures for Association Rules (2015). Available online: http://michael.hahsler.net/research/association_rules/measures.html

2. R. Agrawal, R. Srikant, Fast algorithms for mining association rules, in Proceedings of the 20th International Conference on Very Large Data Bases, VLDB, vol. 1215 (12 Sept 1994), pp. 487–499

3. S. Brin, R. Motwani, J.D. Ullman, S. Tsur, Dynamic itemset counting and implication rules for market basket data. ACM SIGMOD Rec. 26(2), 255–264 (1 June 1997)

4. G. Piatetsky-Shapiro, Discovery, analysis, and presentation of strong rules. Knowl. Discovery Databases 229–238 (1991)

5. Ochin, S. Kumar, N. Joshi, Rule power factor: a new interest measure in associative classification. Procedia Comput. Sci. 93, 12–18 (31 Sept 2016)

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