Mining Association Rules from Stream Data Based on the Dynamic Support

DOI

10.2991/icaita-16.2016.9How to use a DOI?

Keywords

stream data; association rules; inter transaction; support threshold.

Abstract

The Stream data exists in the field of industrial production, life activities, business transactions, and other fields. It is closely related to people’s life, production and so on. This paper proposes inter-transaction association rules mining method based on dynamic support threshold. Inter-transaction association rules refer to the association rules between different time periods. This paper firstly uses the sliding window to limit stream data, then do preprocessing on stream data. In the process of pretreatment using linearization method fitting to raw data and it reduce the amount of data at the same time, and finally at the end of the preprocessing, generating large transaction grouping method of inter transaction association rules is proposed in this paper. This paper uses conceptual data attenuation, thereby reducing the influence of old data to the mining result. Due to artificial setting minimum support threshold may bring many problems, so this paper presents a method for searching minimum support threshold.

Copyright

© 2016, 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  - Jia Luo
AU  - Shihe Chen
AU  - Fengping Pan
AU  - Yaqin Zhu
AU  - Le Wu
AU  - Yaqi Sun
AU  - Chunkai Zhang
PY  - 2016/01
DA  - 2016/01
TI  - Mining Association Rules from Stream Data Based on the Dynamic Support
BT  - Proceedings of the 2016 International Conference on Artificial Intelligence: Technologies and Applications
PB  - Atlantis Press
SP  - 34
EP  - 37
SN  - 1951-6851
UR  - https://doi.org/10.2991/icaita-16.2016.9
DO  - 10.2991/icaita-16.2016.9
ID  - Luo2016/01
ER  -