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Conjecturable knowledge discovery: A fuzzy clustering approach

机译：可猜知的知识发现：模糊聚类方法

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摘要

Traditionally, clustering is the task of dividing objects into homogeneous clusters based on their degrees of similarity. As objects are assigned to clusters, users need to manually give descriptions for all clusters. Characterizing clusters by hand can consume a great deal of time of users. In addition, users sometimes have no specific idea as to how to explain the clustering results; thus, they might give inappropriate descriptions. A clustering technique is proposed to discover conjecturable rules, providing descriptions of clusters with a decision tree classification technique. Every cluster in a conjecturable tree is depicted by only one conjecturable rule. However, less-utilized rules are not necessarily trivial. In some real-life circumstances, there might be some clusters which can be depicted by two or more rules, namely, recessive conjecturable rules. For example, customers usually prefer to buy inexpensive red wines; however, on certain occasions, such for a birthday celebration, they will buy expensive wine. Therefore, we know that there are some people who generally belong to a low-value cluster but may simultaneously be assigned to a high-value one. In this study, we propose a new discovery model for mining conjecturable rules to reveal this type of knowledge. The experimental results show that our proposed model is able to discover conjecturable rules as well as recessive rules. The results of sensitivity analysis are also given for practitioners' reference.
机译：传统上，聚类是根据对象的相似度将对象划分为同质聚类的任务。将对象分配给集群后，用户需要手动提供所有集群的描述。手动表征群集会消耗大量时间。另外，用户有时对如何解释聚类结果没有特定的想法。因此，他们可能会给出不合适的描述。提出了一种聚类技术来发现可猜规则，并通过决策树分类技术为聚类提供描述。可猜想树中的每个聚类仅由一个可猜规则来描述。但是，利用率较低的规则不一定是琐碎的。在现实生活中，可能存在一些可以用两个或多个规则（即隐性可推测规则）描述的类。例如，顾客通常更喜欢购买便宜的红酒；但是，在某些场合，例如生日庆祝会，他们会购买昂贵的葡萄酒。因此，我们知道有些人通常属于低价值集群，但可能同时被分配给高价值集群。在这项研究中，我们提出了一种用于挖掘可猜规则的新发现模型，以揭示这种类型的知识。实验结果表明，我们提出的模型能够发现可猜规则和隐性规则。敏感性分析的结果也提供给从业者参考。

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  • 来源
    《Fuzzy sets and systems》 |2013年第16期|1-23|共23页
  • 作者

    Tony Cheng-Kui Huang; Wu-Hsien Hsu; Yen-Liang Chen;

  • 作者单位

    Department of Business Administration, National Chung Cheng University, 168, University Road, Min-Hsiung, Chia-Yi, Taiwan, Republic of China;

    Gamania Digital Entertainment Co., Ltd., Taiwan, Republic of China;

    Department of Information Management, National Central University, Chung-Li, Taiwan 320, Republic of China;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    data mining; conjecturable rules; fuzzy clustering; classification;

    机译：数据挖掘;可猜想的规则;模糊聚类分类;

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