Abstract
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.
Highlights
Sequential item set mining, is an important task, having many applications with market, customer and web log analysis, item set discovery in protein sequences
It’s quite convincing that for mining frequent item sets, one should mine all the closed ones as the end leads to compact and complete result set having high efficiency [15, 16, 17, 18], unlike mining frequent item sets, there are less methods for mining closed sequential item sets. This is because of intensity of the problem and CloSpan is the only variety of algorithm [17], similar to the frequent closed item set mining algorithms, it follows a candidate maintenance-and-test paradigm, as it maintains a set of readily mined closed sequence candidates used to prune search space and verify whether a recently found frequent sequence is to be closed or not
We show a solution leading to an algorithm, Concurrent Edge Prevision and Rear Edge Pruning (CEG&REP), which can mine efficiently all the sets of frequent closed sequences through a sequence graph protruding approach
Summary
Sequential item set mining, is an important task, having many applications with market, customer and web log analysis, item set discovery in protein sequences. It’s quite convincing that for mining frequent item sets, one should mine all the closed ones as the end leads to compact and complete result set having high efficiency [15, 16, 17, 18], unlike mining frequent item sets, there are less methods for mining closed sequential item sets. This is because of intensity of the problem and CloSpan is the only variety of algorithm [17], similar to the frequent closed item set mining algorithms, it follows a candidate maintenance-and-test paradigm, as it maintains a set of readily mined closed sequence candidates used to prune search space and verify whether a recently found frequent sequence is to be closed or not.
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