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A Modified Mountain Clustering Algorithm based on Hill Valley Function

Author(s): Junnian Wang | Deshun Liu | Chao Liu

Journal: Journal of Networks
ISSN 1796-2056

Volume: 6;
Issue: 6;
Start page: 916;
Date: 2011;
Original page

Keywords: data cluster | hill valley function | mountain clustering method | correlation self-comparison method

A modified mountain clustering algorithm based on the hill valley function is proposed. Firstly, the mountain function is constructed on the data space, with estimating the parameter by a correlation self-comparison method, and database’s mountain function values are computed. Secondly, the hill valley function is introduced to partition the data distributed on each peak. If the hill valley function’ value of two datum equal to 0, it means these two datum are on the same mountain and belong to the same cluster, otherwise they are not. Finally, the data in a cluster with maximum mountain function value is selected as the cluster centre of this cluster. The testing of four databases indicate that the proposed clustering algorithm can categorise the data numbers in each cluster and find all the cluster centres exactly, and no need  priori parameters and stopping criterion correlating to the database.
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