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Sequential Pattern Mining using PrefixSpan with Pseudoprojection and Separator Database

机译：使用pseudoproprect和分离器数据库使用前缀的顺序模式挖掘

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Sequential pattern mining is a new branch of data mining science that solves inter-transaction pattern mining problems. A comprehensive performance study has been reported that PrefixSpan, one of its algorithms, outperforms GSP, SPADE, as well as FreeSpan in most cases, and PrefixSpan integrated with pseudoprojection technique is the fastest among those tested algorithms. Nevertheless, Pseudoprojection technique, which requires maintaining and visiting the in-memory sequence database frequently until all patterns are found, consumes a considerable amount of memory and induces the algorithm to undertake redundant and unnecessary checks to this copy of original database into memory when the candidate patterns are examined. In this paper, we propose Separator Database to improve PrefixSpan with pseudoprojection through early removal of uneconomical in-memory sequence database. The experimental results show that Separator Database improves PrefixSpan with pseudoprojection. Future research includes exploring the use of Separator Database in PrefixSpan with pseudoprojection to improve mining constrained sequential patterns.
机译：顺序模式挖掘是一个新的数据挖掘科学分支，解决了交易间模式挖掘问题。据报道，全面的绩效研究是，在大多数情况下，其算法之一，其算法之一，胜过GSP，Spade以及Freespan，以及与假冒技术集成的前缀是那些测试算法中最快的。然而，需要频繁地维护和访问内存序列数据库的假偶引导，直到找到所有模式，消耗相当大量的内存，并诱导算法在候选者时对此原始数据库的副本进行冗余和不必要的检查。检查模式。在本文中，我们提出了分隔符数据库，通过早期去除不经济内存序列数据库来改善伪锥性的前缀。实验结果表明，分离器数据库改善了假偶突出的前尖。未来的研究包括探索用假偶联的前缀谱系使用分离器数据库，以改善挖掘约束的顺序图案。

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