@inproceedings{1a0271e84cde45a689793135d5977bbd,
title = "Self-adaptive spectral cluster number detecting with particle swarm optimization algorithm",
abstract = "Spectral clustering algorithms have been playing an important role in solving many problems in pattern recognition and image processing. As a well-known spectral clustering algorithm, Normalized Cut has been proved powerful in image segmentation and data clustering. Morever spectral clustering has shown to be more effective in finding clusters than many traditional algorithms such as k-means. However, how to decide the number of clusters is always a crucial problem we confront. It's just yet acknownledge that evolutionary algorithms have a powerful ability to solve such optimization problems. In this paper, we apply a Validity Measure for Fuzzy Clustering(VMFC) to determine the cluster number in spectral clustering with the Particle Swarm Optimization selecting the optimal number of clusters from several possible choices.",
keywords = "Cluster number, Fuzzy c-means clustering, Particle Swarm Optimization, Spectral clustering, Validity measure",
author = "Chupeng Zeng and Aimin Zhou and Guixu Zhang",
note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 2016 IEEE Congress on Evolutionary Computation, CEC 2016 ; Conference date: 24-07-2016 Through 29-07-2016",
year = "2016",
month = nov,
day = "14",
doi = "10.1109/CEC.2016.7744377",
language = "英语",
series = "2016 IEEE Congress on Evolutionary Computation, CEC 2016",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "4607--4611",
booktitle = "2016 IEEE Congress on Evolutionary Computation, CEC 2016",
address = "美国",
}