Discovery of both Direct and Indirect Association Rules in Data Streams with a Sliding Window

DOI

10.2991/icacsei.2013.86How to use a DOI?

Keywords

Direct Association Pattern, Indirect Association Pattern, Data Streams.

Abstract

Association rules mining is a most of important tasks in data mining research. While most of the existing discovery algorithms are dedicated to efficiently mining of frequent patterns, it has been noted recently that some of the infrequent patterns can provide useful insight view into the data. As a result, indirect association rules have been put forward. All the existing algorithms for mining indirect association rules need get all frequent itemsets, and confined to the traditional static database. Instead of this method, we put forward an approach to discover both direct and indirect association rules in data streams with a sliding window. Experiments on the synthetic data stream are made to show the effectiveness and efficiency of the proposed approach.

Copyright

© 2013, 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  - Wei min Ouyang
AU  - Qin hua Huang
PY  - 2013/08
DA  - 2013/08
TI  - Discovery of both Direct and Indirect Association Rules in Data Streams with a Sliding Window
BT  - Proceedings of the 2013 International Conference on Advanced Computer Science and Electronics Information (ICACSEI 2013)
PB  - Atlantis Press
SP  - 339
EP  - 342
SN  - 1951-6851
UR  - https://doi.org/10.2991/icacsei.2013.86
DO  - 10.2991/icacsei.2013.86
ID  - Ouyang2013/08
ER  -