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首页> 外文期刊>Journal of Intelligent Information Systems >Mining sequential patterns from data streams: a centroid approach
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Mining sequential patterns from data streams: a centroid approach

机译：从数据流中挖掘顺序模式：质心方法

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摘要

In recent years, emerging applications introduced new constraints for data mining methods. These constraints are typical of a new kind of data: the data streams. In data stream processing, memory usage is restricted, new elements are generated continuously and have to be considered in a linear time, no blocking operator can be performed and the data can be examined only once. At this time, only a few methods has been proposed for mining sequential patterns in data streams. We argue that the main reason is the combinatory phenomenon related to sequential pattern mining. In this paper, we propose an algorithm based on sequences alignment for mining approximate sequential patterns in Web usage data streams. To meet the constraint of one scan, a greedy clustering algorithm associated to an alignment method is proposed. We will show that our proposal is able to extract relevant sequences with very low thresholds.
机译：近年来，新兴的应用程序为数据挖掘方法引入了新的限制。这些约束是新型数据的典型代表：数据流。在数据流处理中，内存使用受到限制，新元素不断生成，必须在线性时间内考虑，不能执行任何阻塞运算符，并且只能检查一次数据。目前，仅提出了几种方法来挖掘数据流中的顺序模式。我们认为主要原因是与顺序模式挖掘有关的组合现象。在本文中，我们提出了一种基于序列比对的算法，用于挖掘Web使用数据流中的近似顺序模式。为了满足一次扫描的约束条件，提出了一种与对准方法相关的贪婪聚类算法。我们将证明我们的建议能够以非常低的阈值提取相关序列。

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