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：2016
卷号：4
期号：4
页码：7034
DOI：10.15680/IJIRCCE.2016.0404134
出版社：S&S Publications
摘要：The Frequent example mining is one of the essential errands utilized as a part of information mining area and regular information mining methodologies are broadly connected onto static database as well as information stream yet the information have been gathered more rapidly as of late and the relating databases have additionally get to be huger, and subsequently, general regular example mining strategies have been confronted with confinements that don'tproperly react to the monstrous information, so it is important to lead more proficient and prompt mining errands by examining databases just once. Thus, techniques for productively compacting created examples are required keeping in mind the end goal to tackle that issue. we propose a novel algorithm, weighted maximal frequent pattern mining over data streams based on sliding window model (WMFP-SW) to obtain weighted maximal frequent patterns reflecting recent information over data streams
