A robust dynamic niching genetic algorithm with niche migration for automatic clustering problem |
| |
Authors: | Dong-Xia Chang [Author Vitae] Xian-Da Zhang [Author Vitae] [Author Vitae] Dao-Ming Zhang [Author Vitae] |
| |
Affiliation: | a Tsinghua National Laboratory for Information Science and Technology, State Key Laboratory on Intelligent Technology and Systems, Department of Automation, Tsinghua University, Beijing 100084, China b National Key Lab of Integrated Information System Technology, Institute of Software, Chinese Academy of Sciences, Beijing 100080, China c Institute of Information Science, Beijing Jiaotong University, Beijing 100044, China |
| |
Abstract: | In this paper, a genetic clustering algorithm based on dynamic niching with niche migration (DNNM-clustering) is proposed. It is an effective and robust approach to clustering on the basis of a similarity function relating to the approximate density shape estimation. In the new algorithm, a dynamic identification of the niches with niche migration is performed at each generation to automatically evolve the optimal number of clusters as well as the cluster centers of the data set without invoking cluster validity functions. The niches can move slowly under the migration operator which makes the dynamic niching method independent of the radius of the niches. Compared to other existing methods, the proposed clustering method exhibits the following robust characteristics: (1) robust to the initialization, (2) robust to clusters volumes (ability to detect different volumes of clusters), and (3) robust to noise. Moreover, it is free of the radius of the niches and does not need to pre-specify the number of clusters. Several data sets with widely varying characteristics are used to demonstrate its superiority. An application of the DNNM-clustering algorithm in unsupervised classification of the multispectral remote sensing image is also provided. |
| |
Keywords: | Clustering Genetic algorithms Niching method Niche migration Remote sensing image |
本文献已被 ScienceDirect 等数据库收录! |
|