期刊名称：International Journal of Advanced Computer Science and Applications(IJACSA)
印刷版ISSN：2158-107X
电子版ISSN：2156-5570
出版年度：2011
卷号：2
期号：11
DOI：10.14569/IJACSA.2011.021111
出版社：Science and Information Society (SAI)
摘要：Past observations have shown that a frequent item set mining algorithm are purported to mine the closed ones because the finish provides a compact and a whole progress set and higher potency. Anyhow, the newest closed item set mining algorithms works with candidate maintenance combined with check paradigm that is pricey in runtime yet as space usage when support threshold is a smaller amount or the item sets gets long. Here, we show, CEG&REP that could be a capable algorithm used for mining closed sequences while not candidate. It implements a completely unique sequence finality verification model by constructing a Graph structure that build by an approach labeled “Concurrent Edge Prevision and Rear Edge Pruning” briefly will refer as CEG&REP. a whole observation having sparse and dense real-life knowledge sets proved that CEG&REP performs bigger compared to older algorithms because it takes low memory and is quicker than any algorithms those cited in literature frequently.
