An Efficient Approach for Extraction Positive and Negative Association Rules from Big Data

Abstract : Mining association rules is an significant research area in Knowledge Extraction. Although the negative association rules have notable advantages, but they are less explored in comparaison with the positive association rules. In this paper, we propose a new approach allowing the mining of positive and negative rules. We define an efficient method of support counting, called reduction-access-database. Moreover, all the frequent itemsets can be obtained in a single scan over the whole database. As for the generating of interesting association rules, we introduce a new efficient technique, called reduction-rules-space. Therefore, only half of the candidate rules have to be studied. Some experiments will be conducted into such reference databases to complete our study.
Complete list of metadatas

Cited literature [19 references]  Display  Hide  Download

https://hal.inria.fr/hal-02060042
Contributor : Hal Ifip <>
Submitted on : Thursday, March 7, 2019 - 10:36:30 AM
Last modification on : Monday, September 2, 2019 - 9:41:18 AM
Long-term archiving on: Saturday, June 8, 2019 - 1:37:19 PM

File

 Restricted access
To satisfy the distribution rights of the publisher, the document is embargoed until : 2021-01-01

Please log in to resquest access to the document

Licence


Distributed under a Creative Commons Attribution 4.0 International License

Identifiers

Citation

Bemarisika Parfait, Ramanantsoa Harrimann, Totohasina André. An Efficient Approach for Extraction Positive and Negative Association Rules from Big Data. 2nd International Cross-Domain Conference for Machine Learning and Knowledge Extraction (CD-MAKE), Aug 2018, Hamburg, Germany. pp.79-97, ⟨10.1007/978-3-319-99740-7_6⟩. ⟨hal-02060042⟩

Share

Metrics

Record views

338