计算机科学
位图
数据挖掘
任务(项目管理)
集合(抽象数据类型)
国家(计算机科学)
序列模式挖掘
代表(政治)
算法
人工智能
管理
经济
程序设计语言
政治
政治学
法学
作者
Philippe Fournier‐Viger,Antonio Gomariz,Ted Gueniche,Espérance Mwamikazi,Rincy Thomas
标识
DOI:10.1007/978-3-642-53914-5_10
摘要
Sequential pattern mining is a well-studied data mining task with wide applications. However, fine-tuning the minsup parameter of sequential pattern mining algorithms to generate enough patterns is difficult and time-consuming. To address this issue, the task of top-k sequential pattern mining has been defined, where k is the number of sequential patterns to be found, and is set by the user. In this paper, we present an efficient algorithm for this problem named TKS (Top-K Sequential pattern mining). TKS utilizes a vertical bitmap database representation, a novel data structure named PMAP (Precedence Map) and several efficient strategies to prune the search space. An extensive experimental study on real datasets shows that TKS outperforms TSP, the current state-of-the-art algorithm for top-k sequential pattern mining by more than an order of magnitude in execution time and memory.
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