Concurrent Edge Prevision and Rear Edge Pruning Approach for Frequent Closed Itemset Mining

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. KeywordsData mining; Closed Itemsets; Pattern Mining; sequence length; graph structure.