Mining maximal frequent itemsets in a sliding window over data streams

被引：0

作者：

Mao Y. ^{[1
,2
]}

Li H. ^{[1
]}

Yang L. ^{[1
]}

Liu L. ^{[1
]}

机构：

[1] School of Information Science and Engineering, Central South University

[2] Applied Science Institute of Jiangxi University of Science and Technology

来源：

Gaojishu Tongxin/Chinese High Technology Letters | 2010年 / 20卷 / 11期

关键词：

Data mining; Data stream; Frequent itemsets; Maximal frequent itemsets; Sliding window;

D O I：

10.3772/j.issn.1002-0470.2010.11.008

中图分类号：

学科分类号：

摘要：

In consideration of the problem of data and pattern redundancy in frequent itemsets mining and the close attention to the study of mining maximal frequent itemsets from data streams, this paper presents the MMFI-SW algorithm for mining maximal frequent itemsets in a sliding window over data streams. Firstly, it uses a data structure based on FP-tree to record the current information in streams, at the same time, the obsolete items and a lot of infrequent items are deleted by pruning the tree. Then, it designs a novel method to mine the set of all maximal frequent itemsets in a sliding window over data streams. The theoretical analysis and the experimental results show that the proposed method is efficient.

引用

页码：1142 / 1148

页数：6

共 12 条

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