DocumentCode
3372923
Title
Stochastic on-line algorithm versus batch algorithm for quantization and self organizing maps
Author
Fort, Jean-Claude ; Cottrell, Marie ; Letremy, Patrick
Author_Institution
Inst. Elie Cartan, Univ. Nancy 1, Vandoeuvre-les-Nancy, France
fYear
2001
fDate
2001
Firstpage
43
Lastpage
52
Abstract
The Kohonen algorithm (SOM) was originally designed as a stochastic algorithm which works in an on-line way and which was designed to model some adaptative features of the human brain. In fact it is nowadays extensively used for data mining, data visualization, and exploratory data analysis. Some users are tempted to use the batch version of the Kohonen algorithm since it is a deterministic algorithm which can be convenient if one needs to get reproducible results and which can go faster in some cases. In this paper, we try to elucidate the mathematical nature of this batch variant and give some elements of comparison of both algorithms. Then we compare both versions on a real data set
Keywords
self-organising feature maps; Kohonen algorithm; SOM; batch version; data mining; data visualization; exploratory data analysis; stochastic algorithm; Algorithm design and analysis; Brain modeling; Data analysis; Data mining; Euclidean distance; Humans; Quantization; Self organizing feature maps; Signal processing algorithms; Stochastic processes;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks for Signal Processing XI, 2001. Proceedings of the 2001 IEEE Signal Processing Society Workshop
Conference_Location
North Falmouth, MA
ISSN
1089-3555
Print_ISBN
0-7803-7196-8
Type
conf
DOI
10.1109/NNSP.2001.943109
Filename
943109
Link To Document