Clustering based on kernel density estimation: nearest local maximum searching algorithm

• DOI: 10.1016/j.chemolab.2004.02.006
• Publication Date: 2004-04-28T08:30:48Z
• Authors: Wei-Jun Wang; Yong-Xi Tan; Jian-Hui Jiang; Jian-Zhong Lu; Guo-Li Shen; Ru-Qin Yu
• Keywords: Kernel density estimation; Kernel (algebra); Nearest-neighbor chain algorithm; k-medians clustering

Abstract Nearest local maximum searching algorithm (NLMSA), an unsupervised clustering algorithm based on kernel density estimation, is proposed. It is designed for detecting inherent group structures with arbitrary shape clusters among multidimensional measurement data without any a priori information. The algorithm is named after its clustering mechanism of converging data points to their corresponding nearest local maxima of the data's density estimate along the ascending gradient direction. Two simulated data sets and two real data sets are employed to validate the performance of the method. A comparison between the clustering results obtained from the proposed algorithm and the K-means cluster analysis shows that the NLMSA possesses quite satisfactory performance.