Adaptive clustering of stock prices data using cascaded competitivelearning neural networks
Chengyi Sun
Xueli Yu
Xinfang Feng
Comput. Center, Taiyuan Univ. of Technol.;
This paper appears in: Systems, Man, and Cybernetics, 1996., IEEE International Conference on
Publication Date: 14-17 Oct 1996
Volume: 3,
On page(s): 2359-2363 vol.3
Meeting Date: 10/14/1996 - 10/17/1996
Location: Beijing, China
ISBN: 0-7803-3280-6
References Cited: 3
INSPEC Accession Number: 5490825
Digital Object Identifier: 10.1109/ICSMC.1996.565541
Posted online: 2002-08-06 20:46:12.0
Abstract
As part of a stock market analysis and prediction system
consisting of an expert system and neural networks, clustering of stock
prices data is needed. This paper proposes a method of clustering stock
prices data using cascaded competitive learning neural networks. Our
experiments show that the method has achieved effective clustering
results for stock prices data and that the method is easily controlled
to produce clustering results which satisfy the customs of stock market
analysts. The method can be used in the cases of other data which have
intrinsically hierarchical cluster structures
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