A modified mountain clustering algorithm

Authors:
Miin-Shen Yang;Kuo-Lung Wu
Affiliations:
Chung Yuan Christian University, Department of Applied Mathematics, 32023, Chung-Li, Taiwan, ROC;Kun Shan University of Technology, Department of Information Management, 71023, Yung-Kang, Tainan, Taiwan, ROC
Venue:
Pattern Analysis & Applications
Year:
2005

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Abstract

In this paper, we modify the mountain method and then create a modified mountain clustering algorithm. The proposed algorithm can automatically estimate the parameters in the modified mountain function in accordance with the structure of the data set based on the correlation self-comparison method. This algorithm can also estimate the number of clusters based on the proposed validity index. As a clustering tool to a grouped data set, the modified mountain algorithm becomes a new unsupervised approximate clustering method. Some examples are presented to demonstrate this algorithm’s simplicity and effectiveness and the computational complexity is also analyzed.