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Bit Resultant Matrix for Mining Quantitative Association Rules of Bipolar Item Sets

Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 5)

Abstract

Association rule learning is a familiar technique for finding interesting relations that exist between variables in immense databases. Existing mining techniques which are available at present cannot pay attention to negative dependencies and considering only one evaluation criteria for measuring quantity of obtained rules. But for better results, data sets demanding for populating negative associations. To overcome the problem, proposing a technique called Bit Vector (BV) generation for mining quantitative association rules of bipolar (Positive and Negative) item sets together. Proposed system can reduce time complexity of finding recurrent item sets of bipolar association rules and provide more flexibility for finding the frequent item sets.

Keywords

Association rule mining Frequent item sets Infrequent item sets Negative association rule mining Bit vectors 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2017

Authors and Affiliations

  1. 1.Anil Neerukonda Institute of Technology and Sciences (ANITS)VisakhapatnamIndia

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