Document clustering using sequential pattern (SP): Maximal frequent sequences (MFS) as SP representation

D. Rahmawati, G. A. Putri Saptawati, Yani Widyani
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引用次数: 3

Abstract

This research proposes an idea to apply Feature Based Clustering (FBC) in document clustering. A huge number of existing documents will be easier to be used if they are clustered into several topics. FBC uses K-Means algorithm to cluster sequential data of features. Features of text document can be presented as sequence of word. In order to be processed as sequential data, features must be extracted from collection of unstructured text documents. Therefore, we need preprocessing tasks to deliver appropriate form of document features. There are two types of sequential pattern using simple form: Frequent Word Sequence (FWS) and Maximal Frequent Sequence (MFS). Both types are appropriate for text data. The difference is in applying the maximum principle in MFS. Therefore, MFS amount from a text document would be less than the amount of its FWS. In this research, we choose maximal frequent sequences (MFS) as feature representation. We proposes framework to conduct FBC using MFS as features. The framework is tested to cluster dataset that is subset of the Twenty News Group Text Data. The result shows that the accuracy of clustering result is affected by the parameter's value, dataset, and the number of target cluster.
使用顺序模式(SP)的文档聚类:最大频繁序列(MFS)作为SP表示
本文提出了一种基于特征聚类的文档聚类方法。如果将大量现有文档聚集到几个主题中,它们将更容易使用。FBC使用K-Means算法对特征序列数据进行聚类。文本文档的特征可以表示为单词序列。为了将特征作为顺序数据处理,必须从非结构化文本文档集合中提取特征。因此,我们需要预处理任务来提供适当形式的文档特征。使用简单形式的顺序模式有两种:频繁词序列(FWS)和最大频繁序列(MFS)。这两种类型都适用于文本数据。不同之处在于在MFS中应用了极大值原理。因此,来自文本文档的MFS量将小于其FWS的量。在本研究中,我们选择最大频繁序列(MFS)作为特征表示。我们提出了以MFS为特征进行FBC的框架。测试了该框架的聚类数据集,该数据集是20个新闻组文本数据的子集。结果表明,聚类结果的准确性受参数值、数据集和目标聚类数量的影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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