Research On Novel Model of Data Mining Based on Improved Association Rules and Clustering Algorithm

DOI

10.2991/emcs-17.2017.101How to use a DOI?

Keywords

Apriori algorithm; Decision tree; Association rule; Clustering; Data mining

Abstract

Apriori algorithm is one of the most effective algorithms for mining frequent itemsets of Boolean Association rules. Decision tree is a method to analyze and summarize the attributes of a large number of samples. The frequent itemsets are used to generate the association rules, and the strong association rules are generated according to the minimum confidence set by the user. The paper presents research on novel model of data mining based on improved association rules and clustering algorithm. Finally, the effectiveness of the proposed algorithm is verified by experiments.

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/).

Download article (PDF)

TY  - CONF
AU  - Qing Tan
PY  - 2017/03
DA  - 2017/03
TI  - Research On Novel Model of Data Mining Based on Improved Association Rules and Clustering Algorithm
BT  - Proceedings of the 2017 7th International Conference on Education, Management, Computer and Society (EMCS 2017)
PB  - Atlantis Press
SP  - 522
EP  - 526
SN  - 2352-538X
UR  - https://doi.org/10.2991/emcs-17.2017.101
DO  - 10.2991/emcs-17.2017.101
ID  - Tan2017/03
ER  -