Research on Data Mining Framework Based on Improved Sequential Association Rule Discovery

DOI

10.2991/mcei-16.2016.68How to use a DOI?

Keywords

Sequential association rule; Data mining; Apriori algorithm; Clustering; FP tree

Abstract

This paper firstly analyzes the shortcomings of sequential association rule discovery technology, and proposes the improvement method to make up the deficiency. Then, the paper discusses the data mining method based on association rules. The paper presents research on data mining framework based on improved sequential association rule discovery. This novel method can make use of frequent itemsets to generate the required association rules, according to the user set the minimum credibility of the choice, the generation of time sequence association rules.

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  - Qing Tan
PY  - 2016/12
DA  - 2016/12
TI  - Research on Data Mining Framework Based on Improved Sequential Association Rule Discovery
BT  - Proceedings of the 2016 6th International Conference on Mechatronics, Computer and Education Informationization (MCEI 2016)
PB  - Atlantis Press
SP  - 324
EP  - 328
SN  - 1951-6851
UR  - https://doi.org/10.2991/mcei-16.2016.68
DO  - 10.2991/mcei-16.2016.68
ID  - Tan2016/12
ER  -