Fast and Memory Efficient Mining of High Utility Itemsets in Data Streams

Hua Fu Li; Hsin-Yun Huang; Yi-Cheng Chen; Yu-Jiun Liu; Suh-Yin Lee

doi:10.1109/ICDM.2008.107

Fast and Memory Efficient Mining of High Utility Itemsets in Data Streams

Hua Fu Li, Hsin-Yun Huang, Yi-Cheng Chen, Yu-Jiun Liu, Suh-Yin Lee

Department of Computer Science

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

56 Scopus citations

Abstract

Efficient mining of high utility itemsets has become one of the most interesting data mining tasks with broad applications. In this paper, we proposed two efficient one-pass algorithms, MHUI-BIT and MHUI-TID, for mining high utility itemsets from data streams within a transaction-sensitive sliding window. Two effective representations of item information and an extended lexicographical tree-based summary data structure are developed to improve the efficiency of mining high utility itemsets. Experimental results show that the proposed algorithms outperform than the existing algorithms for mining high utility itemsets from data streams.

Original language	English
Title of host publication	ICDM 2008: EIGHTH IEEE INTERNATIONAL CONFERENCE ON DATA MINING, PROCEEDINGS
Publisher	IEEE
Pages	88-+
ISBN (Electronic)	978-0-7695-3502-9
ISBN (Print)	978-0-7695-3502-9
DOIs	https://doi.org/10.1109/ICDM.2008.107
State	Published - 2008
Event	8th IEEE International Conference on Data Mining - Pisa, Italy Duration: 15 Dec 2008 → 19 Dec 2008

Publication series

Name	IEEE International Conference on Data Mining
Publisher	IEEE
ISSN (Print)	1550-4786
ISSN (Electronic)	1550-4786

Conference

Conference	8th IEEE International Conference on Data Mining
Country	Italy
City	Pisa
Period	15/12/08 → 19/12/08

Access to Document

10.1109/ICDM.2008.107

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abstract = "Efficient mining of high utility itemsets has become one of the most interesting data mining tasks with broad applications. In this paper, we proposed two efficient one-pass algorithms, MHUI-BIT and MHUI-TID, for mining high utility itemsets from data streams within a transaction-sensitive sliding window. Two effective representations of item information and an extended lexicographical tree-based summary data structure are developed to improve the efficiency of mining high utility itemsets. Experimental results show that the proposed algorithms outperform than the existing algorithms for mining high utility itemsets from data streams.",

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Li, HF, Huang, H-Y, Chen, Y-C, Liu, Y-J & Lee, S-Y 2008, Fast and Memory Efficient Mining of High Utility Itemsets in Data Streams. in ICDM 2008: EIGHTH IEEE INTERNATIONAL CONFERENCE ON DATA MINING, PROCEEDINGS. IEEE International Conference on Data Mining, IEEE, pp. 88-+, 8th IEEE International Conference on Data Mining, Pisa, Italy, 15/12/08. https://doi.org/10.1109/ICDM.2008.107

Fast and Memory Efficient Mining of High Utility Itemsets in Data Streams. / Li, Hua Fu; Huang, Hsin-Yun; Chen, Yi-Cheng; Liu, Yu-Jiun; Lee, Suh-Yin.

ICDM 2008: EIGHTH IEEE INTERNATIONAL CONFERENCE ON DATA MINING, PROCEEDINGS. IEEE, 2008. p. 88-+ (IEEE International Conference on Data Mining).

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

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