Proceedings of the 2016 International Conference on Computer Science and Electronic Technology

Mining Weighted Rare Association Rules Using Sliding Window over Data Streams

Authors
Weimin Ouyang
Corresponding Author
Weimin Ouyang
Available Online August 2016.
DOI
https://doi.org/10.2991/cset-16.2016.28How to use a DOI?
Keywords
Rare association rules, weighted rare association rules, data streams, sliding window
Abstract
Rare association rules mining is an association rule which has low support and high confidence. In recent years, the problem of mining rare association rules has got quite a lot of attention, which has become a hot topic in data mining research. However, most of the research on mining rare association rules are confined to the static database environment, and treat each item with the same significance although different items may have different significance. In this paper, we propose an algorithm for mining weighted rare association rules over data streams with a sliding window. Experiments on the synthetic data stream show that the proposed algorithm is efficient and scalable.
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This is an open access article distributed under the CC BY-NC license.

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Proceedings
2016 International Conference on Computer Science and Electronic Technology
Part of series
Advances in Computer Science Research
Publication Date
August 2016
ISBN
978-94-6252-213-8
ISSN
2352-538X
DOI
https://doi.org/10.2991/cset-16.2016.28How 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  - Weimin Ouyang
PY  - 2016/08
DA  - 2016/08
TI  - Mining Weighted Rare Association Rules Using Sliding Window over Data Streams
BT  - 2016 International Conference on Computer Science and Electronic Technology
PB  - Atlantis Press
SP  - 116
EP  - 119
SN  - 2352-538X
UR  - https://doi.org/10.2991/cset-16.2016.28
DO  - https://doi.org/10.2991/cset-16.2016.28
ID  - Ouyang2016/08
ER  -