Proceedings of the International Conference on Computer Information Systems and Industrial Applications

An Algorithm of Frequent Patterns Mining Based on Binary Information Granule

Authors
G. Fang, Y. Wu
Corresponding Author
G. Fang
Available Online June 2015.
DOI
https://doi.org/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.
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Proceedings
International Conference on Computer Information Systems and Industrial Applications
Part of series
Advances in Computer Science Research
Publication Date
June 2015
ISBN
978-94-62520-72-1
ISSN
2352-538X
DOI
https://doi.org/10.2991/cisia-15.2015.13How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

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  - International Conference on Computer Information Systems and Industrial Applications
PB  - Atlantis Press
SN  - 2352-538X
UR  - https://doi.org/10.2991/cisia-15.2015.13
DO  - https://doi.org/10.2991/cisia-15.2015.13
ID  - Fang2015/06
ER  -