Using Fuzzy FP-Growth for Mining Association Rules

DOI

10.2991/icoi-17.2017.47How to use a DOI?

Keywords

Data mining, fuzzy association rule, FP-growth

Abstract

This paper aims to use fuzzy set theory and FP-growth derived from fuzzy association rules. At first, we apply fuzzy partition method and decide a membership function of quantitative value for each transaction item. Next, we implement FP-growth to deal with the process of data mining. In addition, in order to understand the impact of fuzzy FP-growth algorithm and other fuzzy data mining algorithms on the execution time and the numbers of generated association rule, the experiment will be performed by using different thresholds. Lastly, the experiment results show fuzzy FP-growth algorithm is more efficient than other existing methods

Copyright

© 2017, 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  - Chien-Hua Wang
AU  - Li Zheng
AU  - Xuelian Yu
AU  - XiDuan Zheng
PY  - 2017/07
DA  - 2017/07
TI  - Using Fuzzy FP-Growth for Mining Association Rules
BT  - Proceedings of the 2017 International Conference on Organizational Innovation (ICOI 2017)
PB  - Atlantis Press
SP  - 275
EP  - 279
SN  - 1951-6851
UR  - https://doi.org/10.2991/icoi-17.2017.47
DO  - 10.2991/icoi-17.2017.47
ID  - Wang2017/07
ER  -