Clustering of non-metric proximity data based on bi-links with €-indiscernibility

Shoji Hirano; Shusaku Tsumoto

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Clustering of non-metric proximity data based on bi-links with €-indiscernibility

机译：基于具有€-不确定性的双向链接的非度量接近度数据的聚类

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摘要

In this paper, we propose a hierarchical grouping method for non-metric proximity data based on bi-links and e-indiscernibility. It hierarchically forms directional links among objects according their directional proximities. A new cluster can be formed when objects in two clusters are connected with bi-directional links (bi-links). The concept of e-indiscernibility is incorporated into the process of establishing bi-links. This scheme enables users to control the level of asymmetry that can be ignored in merging a pair of objects. Experimental results on the soft drink brand switching data showed that this approach is capable of producing better clusters compared to the straightforward use of bi-links.

机译：在本文中，我们提出了一种基于双链接和电子不确定性的非度量邻近数据的分层分组方法。它根据对象之间的方向接近程度在对象之间分层形成方向链接。当两个群集中的对象通过双向链接（bi-links）连接时，可以形成一个新的群集。电子不可区分性的概念已纳入建立双向链接的过程中。该方案使用户能够控制在合并一对对象时可以忽略的不对称程度。关于软饮料品牌转换数据的实验结果表明，与直接使用双向链接相比，这种方法能够产生更好的聚类。

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来源
《Journal of Intelligent Information Systems》 |2013年第1期|61-71|共11页
作者
Shoji Hirano; Shusaku Tsumoto;
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作者单位

Department of Medical Informatics, School of Medicine, Shimane University,89-1 Enya-cho, Izumo, Shimane 693-8501, Japan;

Department of Medical Informatics, School of Medicine, Shimane University,89-1 Enya-cho, Izumo, Shimane 693-8501, Japan;

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正文语种 eng
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关键词
Asymmetric proximity; €-indiscernibility; Clustering;

机译：不对称接近€-不可区分性;聚类;

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Clustering of non-metric proximity data based on bi-links with €-indiscernibility

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