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Autonomous knowledge-oriented clustering Using Decision-Theoretic Rough Set Theory

FUNDAMENTA INFORMATICAE 2012年第2-3期115卷 141-156页

作者： Yu, Hong Chu, Shuangshuang Yang, Dachun Chongqing Univ Posts & Telecommun Inst Comp Sci & Technol Chongqing 400065 Peoples R China ZTE Corp Chongqing R&D Inst Chongqing 400060 Peoples R China

In many applications, clusters tend to have vague or imprecise boundaries. It is desirable that clustering techniques should consider such an issue. The decision-theoretic rough set (DTRS) model is a typical probabilistic rough set model, which has the ability to deal with imprecise, uncertain, and vague information. This paper proposes an autonomous clustering method using the decision-theoretic rough set model based on a knowledge-oriented clustering framework. In order to get the initial knowledge-oriented clustering, the threshold values are produced autonomously based on semantics of clustering without human intervention. Furthermore, this paper estimates the risk of a clustering scheme based on the decision-theoretic rough set by considering various loss functions, which can process the different granular overlapping boundary. An autonomous clustering algorithm is proposed, which is not only experimented with the synthetic data and the standard data but also applied in the web search results clustering. The results of experiments show that the proposed method is effective and efficient.

关键词： clustering knowledge-oriented clustering decision-theoretic rough set theory autonomous data mining

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Autonomous knowledge-oriented clustering Using Decision-Theoretic Rough Set Theory 1

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5th International Conference on Rough Sets and knowledge Technology (RSKT)

ISBN: (数字)9783642162480

ISBN: (纸本)9783642162473

关键词： clustering knowledge-oriented clustering decision-theoretic rough set theory autonomous data mining

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Autonomous clustering Using Rough Set Theory

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International Journal of Automation and computing 2008年第1期5卷 90-102页

作者： Charlotte Bean Chandra Kambhampati Warwick Medical School Gibbet Hill Campus University of WarwickCoventry CV4 7ALUK Department of Computer Science University of HullHull HU6 7RXUK

This paper proposes a clustering technique that minimizes the need for subjective human intervention and is based on elements of rough set theory （RST）. The proposed algorithm is unified in its approach to clustering and makes use of both local and global data properties to obtain clustering solutions. It handles single-type and mixed attribute data sets with ease. The results from three data sets of single and mixed attribute types are used to illustrate the technique and establish its efficiency.

关键词： Rough set theory （RST） data clustering knowledge-oriented clustering autonomous

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knowledge-oriented clustering for decision support

Knowledge-oriented clustering for decision support

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International Joint Conference on Neural Networks

作者： Bean, CL Kambhampati, C Univ Hull Dept Comp Sci Neural Emergent & Agent Technol Grp Kingston Upon Hull HU6 7RX N Humberside England

ISBN: (纸本)0780378989

Cluster Analysis is traditionally an unsupervised data reduction technique. However, by unifying ideas from both Cluster Analysis and Rough Set Theory, the inherent structure of a data set can be expressed as a rule set which, in turn, can be modified in a supervised manner to obtain a decision rule set with minimal ambiguity. This process of encasing knowledge extraction in an algorithmic framework results in an optimal process for decision support.

关键词： Rough Set Theory knowledge-oriented clustering ambiguity decision support

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