Mining frequent pattern based on fading factor in data streams

Jia Dong Ren; Hui Ling He; Chang Zhen Hu; Li Na Xu; Li Bo Wang

doi:10.1109/ICMLC.2009.5212115

Mining frequent pattern based on fading factor in data streams

Jia Dong Ren^*, Hui Ling He, Chang Zhen Hu, Li Na Xu, Li Bo Wang

^*Corresponding author for this work

School of Cyberspace Science and Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Citations (Scopus)

Abstract

In order to improve the mining efficiency of frequent patterns in data streams, we present an algorithm DS-FPM for mining frequent patterns in data streams. First, a data structure DSFP-tree is constructed and the data stream is divided into a set of segments, then potential frequent itemsets on each segment are obtained by IGFA algorithm, while the generated itemsets and the remaining itemsets of DSFP-tree generated by the earlier segment and sampled by fading factor are stored in new DSFP-tree, finally, the frequent patterns in the data stream can be rapidly found by a breadth-first search strategy. The experimental result shows that the execution efficiency of DS-FPM is better than that of FPIL-STREAM algorithm.

Original language	English
Title of host publication	Proceedings of the 2009 International Conference on Machine Learning and Cybernetics
Pages	2250-2254
Number of pages	5
DOIs	https://doi.org/10.1109/ICMLC.2009.5212115
Publication status	Published - 2009
Event	2009 International Conference on Machine Learning and Cybernetics - Baoding, China Duration: 12 Jul 2009 → 15 Jul 2009

Publication series

Name	Proceedings of the 2009 International Conference on Machine Learning and Cybernetics
Volume	4

Conference

Conference	2009 International Conference on Machine Learning and Cybernetics
Country/Territory	China
City	Baoding
Period	12/07/09 → 15/07/09

Keywords

Data streams
Fading factor
Frequent pattern

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10.1109/ICMLC.2009.5212115

Cite this

Ren, J. D., He, H. L., Hu, C. Z., Xu, L. N., & Wang, L. B. (2009). Mining frequent pattern based on fading factor in data streams. In Proceedings of the 2009 International Conference on Machine Learning and Cybernetics (pp. 2250-2254). Article 5212115 (Proceedings of the 2009 International Conference on Machine Learning and Cybernetics; Vol. 4). https://doi.org/10.1109/ICMLC.2009.5212115

@inproceedings{ddd2be3888d54462b008c708cad0c706,

title = "Mining frequent pattern based on fading factor in data streams",

abstract = "In order to improve the mining efficiency of frequent patterns in data streams, we present an algorithm DS-FPM for mining frequent patterns in data streams. First, a data structure DSFP-tree is constructed and the data stream is divided into a set of segments, then potential frequent itemsets on each segment are obtained by IGFA algorithm, while the generated itemsets and the remaining itemsets of DSFP-tree generated by the earlier segment and sampled by fading factor are stored in new DSFP-tree, finally, the frequent patterns in the data stream can be rapidly found by a breadth-first search strategy. The experimental result shows that the execution efficiency of DS-FPM is better than that of FPIL-STREAM algorithm.",

keywords = "Data streams, Fading factor, Frequent pattern",

author = "Ren, {Jia Dong} and He, {Hui Ling} and Hu, {Chang Zhen} and Xu, {Li Na} and Wang, {Li Bo}",

year = "2009",

doi = "10.1109/ICMLC.2009.5212115",

language = "English",

isbn = "9781424437030",

series = "Proceedings of the 2009 International Conference on Machine Learning and Cybernetics",

pages = "2250--2254",

booktitle = "Proceedings of the 2009 International Conference on Machine Learning and Cybernetics",

note = "2009 International Conference on Machine Learning and Cybernetics ; Conference date: 12-07-2009 Through 15-07-2009",

}

Ren, JD, He, HL, Hu, CZ, Xu, LN & Wang, LB 2009, Mining frequent pattern based on fading factor in data streams. in Proceedings of the 2009 International Conference on Machine Learning and Cybernetics., 5212115, Proceedings of the 2009 International Conference on Machine Learning and Cybernetics, vol. 4, pp. 2250-2254, 2009 International Conference on Machine Learning and Cybernetics, Baoding, China, 12/07/09. https://doi.org/10.1109/ICMLC.2009.5212115

Mining frequent pattern based on fading factor in data streams. / Ren, Jia Dong; He, Hui Ling; Hu, Chang Zhen et al.
Proceedings of the 2009 International Conference on Machine Learning and Cybernetics. 2009. p. 2250-2254 5212115 (Proceedings of the 2009 International Conference on Machine Learning and Cybernetics; Vol. 4).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Mining frequent pattern based on fading factor in data streams

AU - Ren, Jia Dong

AU - He, Hui Ling

AU - Hu, Chang Zhen

AU - Xu, Li Na

AU - Wang, Li Bo

PY - 2009

Y1 - 2009

N2 - In order to improve the mining efficiency of frequent patterns in data streams, we present an algorithm DS-FPM for mining frequent patterns in data streams. First, a data structure DSFP-tree is constructed and the data stream is divided into a set of segments, then potential frequent itemsets on each segment are obtained by IGFA algorithm, while the generated itemsets and the remaining itemsets of DSFP-tree generated by the earlier segment and sampled by fading factor are stored in new DSFP-tree, finally, the frequent patterns in the data stream can be rapidly found by a breadth-first search strategy. The experimental result shows that the execution efficiency of DS-FPM is better than that of FPIL-STREAM algorithm.

AB - In order to improve the mining efficiency of frequent patterns in data streams, we present an algorithm DS-FPM for mining frequent patterns in data streams. First, a data structure DSFP-tree is constructed and the data stream is divided into a set of segments, then potential frequent itemsets on each segment are obtained by IGFA algorithm, while the generated itemsets and the remaining itemsets of DSFP-tree generated by the earlier segment and sampled by fading factor are stored in new DSFP-tree, finally, the frequent patterns in the data stream can be rapidly found by a breadth-first search strategy. The experimental result shows that the execution efficiency of DS-FPM is better than that of FPIL-STREAM algorithm.

KW - Data streams

KW - Fading factor

KW - Frequent pattern

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SN - 9781424437030

T3 - Proceedings of the 2009 International Conference on Machine Learning and Cybernetics

SP - 2250

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BT - Proceedings of the 2009 International Conference on Machine Learning and Cybernetics

T2 - 2009 International Conference on Machine Learning and Cybernetics

Y2 - 12 July 2009 through 15 July 2009

ER -

Mining frequent pattern based on fading factor in data streams

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