期刊名称：International Journal of Innovative Research in Computer and Communication Engineering
印刷版ISSN：2320-9798
电子版ISSN：2320-9801
出版年度：2013
卷号：1
期号：3
出版社：S&S Publications
摘要：Data stream is a continuous, real time, ordered sequence of items. In data stream, data arrives endlessly and the volume of data can be potentially infinite. In recent years advances in hardware and software technologies have resulted in automated storage of data fro m a variety of process. Data mining techniques are applied in data streams to find out the significant knowledge. The term data mining refers, to find relevant and useful information fro m large database. Some of the important techniques in data mining are association rule, classification, clustering, frequent episodes, and deviation detection. Frequent pattern mining is used to find important frequent patterns from the large dataset. Click stream analysis, market basket analysis, web link enquiry, genome study, network monitoring and medicine designing are some of the important areas where frequent pattern mining is used. Mo st commonly used frequent pattern mining algorithms are Apriori, partition algorithm, pincer- search algorithm, fp-growth algorithm, dynamic item set counting algorithm and so on. In this research paper éclat and Rapid Association Rule mining algorithm are used for finding the frequent item sets in data streams. The experimental results show that the performance of RARM algorithm is better than éclat
关键词：Association rules mining; Data mining; Data streams; éclat algorithm; frequent pattern mining; RA RM ; algorithm
