Proceedings of the 2016 6th International Conference on Mechatronics, Computer and Education Informationization (MCEI 2016)

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

Authors
Qing Tan
Corresponding Author
Qing Tan
Available Online December 2016.
DOI
https://doi.org/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.
Open Access
This is an open access article distributed under the CC BY-NC license.

Download article (PDF)

Proceedings
2016 6th International Conference on Mechatronics, Computer and Education Informationization (MCEI 2016)
Part of series
Advances in Intelligent Systems Research
Publication Date
December 2016
ISBN
978-94-6252-282-4
ISSN
1951-6851
DOI
https://doi.org/10.2991/mcei-16.2016.68How 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  - Qing Tan
PY  - 2016/12
DA  - 2016/12
TI  - Research on Data Mining Framework Based on Improved Sequential Association Rule Discovery
BT  - 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  - https://doi.org/10.2991/mcei-16.2016.68
ID  - Tan2016/12
ER  -