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Information clustering based on fuzzy multisets

机译:基于模糊多集的信息聚类

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A fuzzy multiset model for information clustering is proposed with application to information retrieval on the World Wide Web. Noting that a search engine retrieves multiple occurrences of the same subjects with possibly different degrees of relevance, we observe that fuzzy multisets provide an appropriate model of information retrieval on the WWW. Information clustering which means both term clustering and document clustering is considered. Three methods of the hard c-means, fuzzy c-means, and an agglom-erative method using cluster centers are proposed. Two distances between fuzzy multisets and algorithms for calculating cluster centers are denned. Theoretical properties concerning the clustering algorithms are studied. Illustrative examples are given to show how the algorithms work.
机译:提出了一种模糊聚类的信息聚类模型,并将其应用于万维网上的信息检索。注意到搜索引擎以相同的相关程度检索到多个相同主题的事件,我们注意到模糊多集为WWW上的信息检索提供了合适的模型。信息聚类意味着术语聚类和文档聚类。提出了硬c-均值,模糊c-均值和聚类中心聚类的三种方法。确定了模糊多集与计算聚类中心的算法之间的两个距离。研究了与聚类算法有关的理论性质。给出了说明性示例以说明算法如何工作。

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