This paper processes an autonomous knowledge-oriented clustering method based on the decision-theoretic rough set theory model. In order to get the initial clustering of knowledge-oriented clusterings, the threshold values are produced autonomously in view of physics theory in this paper rather than are subjected by human intervention. Furthermore, this paper proposes a cluster validity index based on the decision-theoretic rough set theory model by considering various loss functions. Experiments with synthetic and standard data show that the novel method is not only helpful to select a termination point of the clustering algorithm, but also is useful to cluster the overlapped boundaries which is common in many data mining applications. © 2010 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
Yu, H., Chu, S., & Yang, D. (2010). Autonomous knowledge-oriented clustering using decision-theoretic rough set theory. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6401 LNAI, pp. 687–694). https://doi.org/10.1007/978-3-642-16248-0_93
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