I. Introduction
As a key technique of pattern recognition, clustering analysis plays an important and broad role and has been widely applied in many fields [1], [2], [3], including image processing/machine vision, robot sensing, statistics, bioinformatics, data mining, and machine learning. Many clustering methods have been proposed and great technical progresses have been achieved [1], [2], [3], [4], such as K-means, density-based spatial clustering of applications with noise (DBSCAN), spectral clustering, and so on. However, with the rapid development of modern technology, the requirements of pattern recognition become higher and higher. The conventional clustering methods could not meet the increasing requirements in modern society.