Abstract
This paper addresses the problem of dependency mining in large sets. The first goal is to determine and reduce the dimension of data using principal component analysis. The second is to group variables into several classes using Kohonen’s self-organizing maps and then the K-means algorithm. Evaluations have been performed on 350 financial trading rules (variables) observed in a period of 1300 instants (observations). It was shown that the rules are strongly correlated, all of which can be reproduced from 150 generators with an accuracy of 95%. Moreover, the initial set of 350 rules was subdivided into 23 classes of similar rules.
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Lipinski, P. (2005). Dependency Mining in Large Sets of Stock Market Trading Rules. In: Pejaś, J., Piegat, A. (eds) Enhanced Methods in Computer Security, Biometric and Artificial Intelligence Systems. Springer, Boston, MA. https://doi.org/10.1007/0-387-23484-5_32
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DOI: https://doi.org/10.1007/0-387-23484-5_32
Publisher Name: Springer, Boston, MA
Print ISBN: 978-1-4020-7776-0
Online ISBN: 978-0-387-23484-7
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