Multivariable Statistical Correlation Measure Applied to Association Rules Mining

DOI

10.2991/cisia-15.2015.266How to use a DOI?

Keywords

data mining;association rules;M-correlation; FP-Forest

Abstract

Correlation is usually used in the context of real-valued sequences. However, in data mining, the values range may be of various types-real, nominal or ordinal. Regardless of their type, the methods on measuring correlation between multivariable sequences of data are reviewed. In particular, a new method on measuring the statistical correlation of multivariable sequences is proposed. As the method relies on the geometrical meaning of dot conduct to get the degree of multivariable correlation, it is called M-correlation. M-correlation is used to cut redundancy association rules in this paper. In order to enhance mining efficiency, a novel algorithm, namely FT-Miner, is presented to find all frequent sub-trees in a forest, using two new data structures called UFP-Tree and FP-Forest. The experimentation shows that the algorithm not only reduces a lot of unavailable rules, but also has better capability than classical algorithms.

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  - J. Hu
AU  - H.F Jian
AU  - J.H Sun
PY  - 2015/06
DA  - 2015/06
TI  - Multivariable Statistical Correlation Measure Applied to Association Rules Mining
BT  - Proceedings of the International Conference on Computer Information Systems and Industrial Applications
PB  - Atlantis Press
SP  - 983
EP  - 985
SN  - 2352-538X
UR  - https://doi.org/10.2991/cisia-15.2015.266
DO  - 10.2991/cisia-15.2015.266
ID  - Hu2015/06
ER  -