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OALib Journal期刊
ISSN: 2333-9721
费用：99美元
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全粒度聚类算法
DOI: 10.13232/j.cnki.jnju.2014.04.015
Keywords: 相似性度量,聚类分析,全粒度
Abstract:
聚类分析是数据挖掘与知识发现领域的一个重要研究方向。多数聚类算法中相似性是其核心概念之一，对象之间的相似性会被直接或者间接的计算出来。传统的相似性度量方法多是基于单一的粒度去观察两个被测对象。在人类认知过程中，通常采用多粒度来更合理有效地进行问题求解。本文借鉴人类的这种多粒度认知机理，提出一种新的相似性学习方法，称作全粒度相似性度量方法，基于此发展了一种全粒度聚类算法。而全粒度相似性度量从各个角度观察被测对象，进而会得到两个对象间更加真实的相似度。从uci数据集中选取5组数据进行实验，最后通过与两种传统的聚类方法比较验证了全粒度聚类算法的合理性与有效性。
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