Incremental updates of closed frequent itemsets over continuous data streams

被引：36

作者：

Li, Hlia-Fu ^{[1
]}

Ho, Chin-Chuan ^{[2
]}

Lee, Suh-Yin ^{[2
]}

机构：

[1] Kainan Univ, Dept Comp Sci, Tao Yuan, Taiwan

[2] Natl Chiao Tung Univ, Dept Comp Sci, Hsinchu, Taiwan

来源：

EXPERT SYSTEMS WITH APPLICATIONS | 2009年 / 36卷 / 02期

关键词：

Data mining; Data streams; Closed frequent itemsets; Single-pass mining; Incremental update;

D O I：

10.1016/j.eswa.2007.12.054

中图分类号：

TP18 [人工智能理论];

学科分类号：

140502 [人工智能];

摘要：

Online mining of closed frequent itemsets over streaming data is one of the most important issues in mining data streams. In this paper, we propose an efficient one-pass algorithm, NewMoment to maintain the set of closed frequent itemsets in data streams with a transaction-sensitive sliding window. An effective bit-sequence representation of items is used in the proposed algorithm to reduce the time and memory needed to slide the windows. Experiments show that the proposed algorithm not only attain highly accurate mining results. but also run significant faster and consume less memory than existing algorithm Moment for mining closed frequent itemsets over recent data streams (C) 2007 Elsevier Ltd. All rights reserved.

引用

页码：2451 / 2458

页数：8

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