关键词: 聚类分析, 流形学习, 数据挖掘, 基于密度的聚类

Abstract: Density based clustering are effective and basic clustering techniques for data with spatial attributes. Although there are many proposed algorithms and applications for density a based spatial clustering, one of its widely used assumptions is that the data is distributed in a smooth space. Manifolds are approximately curved spaces which are locally like smooth spaces but not smooth spaces. The purpose of this paper is to propose new density based clustering algorithms on manifolds, that is, on curved spaces. The newly proposed algorithms will apply for nonlinear, non-uniform data distribution. A simple performance analysis is presented. Experimental data are given for testing the performances of the new algorithms.

Key words: cluster analysis, manifold learning, data mining, density-based