Abstract
This paper proposes an improved model of chaotic neural network used to cluster high-dimensional datasets with cross sections in the feature space. A thorough study was designed to elucidate the possible behavior of hundreds interacting chaotic oscillators. New synchronization type - fragmentary synchronization within cluster elements dynamics was found. The paper describes a method for detecting fragmentary synchronization and it’s advantages when applied to data mining problem.
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Benderskaya, E.N., Zhukova, S.V. (2009). Fragmentary Synchronization in Chaotic Neural Network and Data Mining. In: Corchado, E., Wu, X., Oja, E., Herrero, Á., Baruque, B. (eds) Hybrid Artificial Intelligence Systems. HAIS 2009. Lecture Notes in Computer Science(), vol 5572. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02319-4_38
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DOI: https://doi.org/10.1007/978-3-642-02319-4_38
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-02318-7
Online ISBN: 978-3-642-02319-4
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