Hierarchical Clustering Algorithm of the Minimum Risk

Abstract:

Clustering analysis is grouping a set of physical or abstract objects into the similar class. In traditional clustering algorithm, objects are usually divided into a certain cluster. This paper applies the risk evaluation of decision-theoretic rough set model in clustering analysis which solves the problem of uncertain boundary region, and proposes a hierarchical clustering algorithm of the minimum risk which can adjust threshold value to construct a clustering evaluation function in order to find the solution to optimize the result. At last, the case analysis shows the algorithm is feasible. It could provide a strong support for marine environment monitoring system and so on.

• Periodical: Applied Mechanics and Materials (Volumes 333-335)
• Pages: 1410-1413
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.333-335.1410
• Online since: July 2013
• Authors: De Xing Wang, Jie Long Xu, Hong Chun Yuan
• Keywords: Hierarchical Clustering, Risk, Rough Set (RS)

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