Yager和Filev( 1994 )介紹了山峰類聚法，並且由Yang和Wu( 2005 )所推廣。該方法是基於在特徵空間的密度估算，誰的山峰最高就當成該群的群心。這個過程一直重複到條件符合我們所需。本篇我們推廣一個有權重的聚類山峰法，在有干擾點的情況下能得到較好的效果。該方法也可以使用在起始群心的取得。根據Liu和Yang(1994)的彩色影像分割的結果證明，該方法比使用隨機取得群心的方法還要好。

The Mountain Method of clustering was introduced by Yager and Filev (1994) and modified by Yang and Wu (2005). The approach is based on density estimation in feature space with the highest peak extracted as a cluster center and a new density estimation created for extraction of the next cluster center. The process is repeated until a stopping condition is met. In this paper, we develop a weighted mountain clustering algorithm that works well in noise variables. It can be useful for obtaining the initial estimate cluster centers for other more complex clustering algorithms. Results from color image segmentation illustrate the proposed methods produce better segmentation than the randomly generated initial cluster centers according to Liu and Yang’s (1994) evaluation function.

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