K-means Algorithm Based on Fitting Function

DOI

10.2991/asei-15.2015.383How to use a DOI?

Keywords

Density,Optimal distance, Fitting function,K-means

Abstract

The K-means algorithm has the shortcomings of being sensitive to the initial clustering center, and in order to overcome this drawback, in this paper ,on the basis of the combination of data density and the optimal distance , a new definition of fitting function is made and then a kind of K-means algorithm based on fitting function is proposed. By utilizing the fitting function to select the initial clustering center, the selection of the initial cluster centers can be made as much close to the real sample clustering centers as possible. The experiments proved that, the K-means algorithm based on fitting function reduces the number of iterations and enhances the stability of the algorithm, as well as improves the efficiency of the algorithm.

Copyright

© 2015, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - SiYong Chu
AU  - YanNi Deng
AU  - LinLi Tu
PY  - 2015/05
DA  - 2015/05
TI  - K-means Algorithm Based on Fitting Function
BT  - Proceedings of the 2015 International conference on Applied Science and Engineering Innovation
PB  - Atlantis Press
SP  - 1940
EP  - 1945
SN  - 2352-5401
UR  - https://doi.org/10.2991/asei-15.2015.383
DO  - 10.2991/asei-15.2015.383
ID  - Chu2015/05
ER  -