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通过增量聚类预处理分区的一种序列模式挖掘方法

A Partition-based Approach for Sequential Patterns Mining Based on Incremental Clustering Pre-processing
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摘要 大多序列模式挖掘算法在处理呈指数增长的模式时性能有限,而且当输入的数据集很大时,因为主存限制将使其变成不可解的。本文提出基于分区的序列模式挖掘算法,克服了主存限制的缺点,并通过增量聚类方法对数据预处理,得到更合理的分区以提高整体性能。 Most methods show limited performance due to the exponential number of growing patterns. Moreover when the input data set is very large, it is unsolvable because of main memory limitation. This paper shows a partition-based approach to overcome this drawback, and uses pre-processing method based on incremental clustering to get seemly partitions.
作者 吴楠
机构地区 合肥工业大学计算机与信息学院
出处 《宿州学院学报》 2008年第2期102-103,133,共3页 Journal of Suzhou University
基金 安徽省教育厅教学研究项目(2007jyxm453)
关键词 数据挖掘 序列模式 分区算法 增量聚类 Data mining Sequential pattern Partition-based approach Incremental clustering
分类号 TP274 [自动化与计算机技术—检测技术与自动化装置]
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宿州学院学报
2008年 第2期

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