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An analysis of efficient clustering methods for estimates similarity measures

机译:估计相似性度量的有效聚类方法分析

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The main objective of clustering to form a group of similar/dissimilar data object into cluster. Cluster analysis aim to group a collection of patterns in to cluster based on similarity. Cluster is the unsupervised learning technique which is used to looping a set of unordered data object in to a smaller number of meaning full cluster. The relation between cluster either intra or inter. Clustering is mostly analysis for field of text document. In this domain problem finds many applications in Market Analysis, web mining and indexing. In this analysis covers of clustering methods similarity measures based on distance. To discover related work this cluster technique find a new proposal for our further work in text documents, similarity meaning data mining.
机译:聚类的主要目的是将一组相似/不相似的数据对象组成聚类。聚类分析旨在基于相似性将一组模式集合分组到聚类中。聚类是一种无监督的学习技术,用于将一组无序数据对象循环到数量较少的完整聚类中。内部或内部群集之间的关系。聚类主要是对文本文档领域的分析。在这个领域中,问题在市场分析,网络挖掘和索引编制中发现了许多应用。在本分析中,将介绍基于距离的聚类方法的相似性度量。为了发现相关工作,该聚类技术为我们在文本文档中的进一步工作找到了新建议,相似性意味着数据挖掘。

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