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An optimal record retrieval technique in text mining using GSO-based prefix span algorithm and improved K-means

机译:基于GSO的前缀跨度算法和改进的K均值的文本挖掘中的最佳记录检索技术

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摘要

The data mining, nowadays, has surfaced as an investigative procedure devoted to the exploration of the data in the hunt for the reliable patterns or methodical associations between the variables. In the current investigation, an earnest effort made to employ an effective hybrid clustering approach to cluster the record and regain the record in accordance with the pattern mining. The novel technique consists of two vital steps such as the training and testing stages. In the training stage, the closed itemsets of each record are extorted by means of the support values, paving the way for the incredible decrease in the error making items. In the testing stage, the records having identical or approximately identical weights are clustered by means of the hybrid K-means-GSO clustering algorithm. Consequently, records at the apex of rank list are regained from the testing stage. The epoch-making technique is performed in the Java platform.
机译:如今,数据挖掘已作为一种调查程序浮出水面,专门用于探索数据,以寻找变量之间的可靠模式或系统的关联。在当前的调查中,我们竭尽全力采用有效的混合聚类方法对记录进行聚类,并根据模式挖掘重新获得记录。新技术包括两个重要步骤,例如培训和测试阶段。在训练阶段,每个记录的封闭项目集通过支持值被勒索,从而为错误减少项目的不可思议的减少铺平了道路。在测试阶段,通过混合K-means-GSO聚类算法对具有相同或近似相同权重的记录进行聚类。因此,从测试阶段重新获得排名最高的记录。划时代的技术是在Java平台中执行的。

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