An Algorithm of Frequent Patterns Mining Based on Binary Information Granule

DOI

10.2991/cisia-15.2015.13How to use a DOI?

Keywords

binary; frequent patterns; association rules; data mining; granular computing

Abstract

To get rid of these traditional frameworks for discovering frequent association patterns, this paper proposes an algorithm of frequent association patterns mining based on binary information granule, which is mainly different from the Apriori framework and the FP-growth framework. The algorithm generate candidate by Boolean complementation to avoid connecting candidate operation of the Apriori framework, and compute support by the intersection of binary information granules to avoid to repeatedly read the database; it also adopts a linear array to avoid using complex data structure similar to the FP-growth framework. Based on these comparisons of experiments, the results indicate that the proposed algorithm is better than the traditional mining frameworks, particularly, the Apriori framework and the FP-growth framework.

Copyright

© 2015, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - G. Fang
AU  - Y. Wu
PY  - 2015/06
DA  - 2015/06
TI  - An Algorithm of Frequent Patterns Mining Based on Binary Information Granule
BT  - Proceedings of the International Conference on Computer Information Systems and Industrial Applications
PB  - Atlantis Press
SP  - 47
EP  - 50
SN  - 2352-538X
UR  - https://doi.org/10.2991/cisia-15.2015.13
DO  - 10.2991/cisia-15.2015.13
ID  - Fang2015/06
ER  -