Mining Weighted Association Rules in Data Streams with a Sliding Window

DOI

10.2991/csic-15.2015.65How to use a DOI?

Keywords

Association rules, Weighted association rules, Data streams

Abstract

Association rule mining is one of the most important data mining techniques. Typical association rules consider each item in transactions with the same significance.In order to represent significances of items, every item has be assigned with a weight, and the mining weighted association rules have been proposed. All of the literature on weighted association rules mining, to our best knowledge, is confined to the traditional, relatively static database environment; no research work has been conducted on mining weighted association rules over data streams. In this paper, we propose an algorithm for mining weighted association rules over data streams. Experiments on the synthetic data stream are made to show the effectiveness and efficiency of the proposed approach.

Copyright

© 2015, 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  - Weimin Ouyang
AU  - Qinhua Huang
PY  - 2015/07
DA  - 2015/07
TI  - Mining Weighted Association Rules in Data Streams with a Sliding Window
BT  - Proceedings of the 2015 International Conference on Computer Science and Intelligent Communication
PB  - Atlantis Press
SP  - 271
EP  - 274
SN  - 2352-538X
UR  - https://doi.org/10.2991/csic-15.2015.65
DO  - 10.2991/csic-15.2015.65
ID  - Ouyang2015/07
ER  -