文章基本信息

标题：Mining Maximum frequent item sets over data streams using Transaction Sliding Window Techniques
本地全文：下载
作者：ANNURADHA DHULL ; NEERAJ YADAV
期刊名称：International Journal of Computer Science and Network Security
印刷版ISSN：1738-7906
出版年度：2014
卷号：14
期号：2
页码：85-90
出版社：International Journal of Computer Science and Network Security
摘要：As we know that the online mining of streaming data is one of the most important issues in data mining. In this paper, we proposed an efficient one- .frequent item sets over a transaction-sensitive sliding window), to mine the set of all frequent item sets 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. The experiments show that the proposed algorithm not only attain highly accurate mining results, but also the performance significant faster and consume less memory than existing algorithms for mining frequent item sets over recent data streams. In this paper our theoretical analysis and experimental studies show that the proposed algorithm is efficient and scalable and perform better for mining the set of all maximum frequent item sets over the entire history of the data streams.
关键词：recently frequent item sets; maximum frequent item set transaction sliding window; data stream; mining data stream