DSM-TKP:在Web点击流上挖掘top-k路径遍历模式

Hua-Fu Li, Suh-Yin Lee, M. Shan
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引用次数: 16

摘要

在线上,单遍挖掘Web点击流提出了一些有趣的计算问题,例如流数据的无限长度,可能非常快的到达率以及只需对先前到达的点击序列器进行一次扫描。在本文中,我们提出了一种新的单遍算法,称为DSM-TKP(数据流挖掘top-k路径遍历模式),用于挖掘top-k路径遍历模式,其中k是要挖掘的路径遍历模式的期望数量。使用一种称为TKP-forest (top-k path forest)的有效汇总数据结构来维护迄今为止关于点击流的top-k路径遍历模式的基本信息。实验研究表明,DSM-TKP算法使用稳定的内存,并且只对流数据进行一次传递。
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DSM-TKP: mining top-k path traversal patterns over Web click-streams
Online, single-pass mining Web click streams poses some interesting computational issues, such as unbounded length of streaming data, possibly very fast arrival rate and just one scan over previously arrived click-sequencer In this paper, we propose a new, single-pass algorithm, called DSM-TKP (data stream mining for top-k path traversal patterns), for mining top-k path traversal patterns, where k is the desired number of path traversal patterns to be mined. An effective summary data structure called TKP-forest (top-k path forest) is used to maintain the essential information about the top-k path traversal patterns of the click-stream so far. Experimental studies show that DSM-TKP algorithm uses stable memory usage and makes only one pass over the streaming data.
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