DocumentCode :
2621228
Title :
Automatic selection of the number of clusters in multidimensional data problems
Author :
Marazzi, A. ; Gamba, P. ; Mecocci, A. ; Semboloni, A.
Author_Institution :
Dipt. di Ingegneria Elettronica, Pavia Univ., Italy
Volume :
3
fYear :
1996
fDate :
16-19 Sep 1996
Firstpage :
631
Abstract :
When processing multidimensional remote sensing data, one of the main problem is the choice for the appropriate number of clusters; despite of the great number of good algorithms for clustering, each of them works properly only when the appropriate number of clusters is selected. As adaptive versions of the K-means, competitive learning (CL) algorithms also have a similar crucial problem; various efforts to improve the performance of CL were made with the introduction of frequency sensitive competitive learning (FSCL) and rival penalised competitive learning (RPCL). We present an improvement of the RPCL algorithm well adapted to work with every kind of real clustering data problems. The basic idea of this new algorithm is to introduce a competition also between the weights. The algorithm was tested on multiband images with different weights initial position, giving similar results
Keywords :
adaptive signal processing; pattern recognition; remote sensing; unsupervised learning; RPCL algorithm; adaptive K-means competitive learning algorithm; automatic cluster selection; frequency sensitive competitive learning; multiband images; multidimensional data problems; multidimensional remote sensing data processing; rival penalised competitive learning; weights competition; Clustering algorithms; Data processing; Frequency; Multidimensional systems; Partitioning algorithms; Power capacitors; Remote sensing; Robustness; Testing;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Image Processing, 1996. Proceedings., International Conference on
Conference_Location :
Lausanne
Print_ISBN :
0-7803-3259-8
Type :
conf
DOI :
10.1109/ICIP.1996.560574
Filename :
560574
Link To Document :
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