Algorithms for Real-Time Clustering and Generation of Rules from Data

Filev, Dimitar and Angelov, Plamen (2007) Algorithms for Real-Time Clustering and Generation of Rules from Data. In: Advances in Fuzzy Clustering and Its Applications. John Willey and Sons, Chichester, pp. 353-370. ISBN 978-0-470-02760-8

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The problem of real-time clustering has gained considerable attention in recent years in conjunction with the advances in the areas of multiple model representation of complex systems, summarization of information, and novelty detection for diagnostics and prognostics. This chapter deals with two main approaches for real-time clustering – the first algorithm is density based and is derived from the Mountain/Subtractive clustering method while the second one is distance based and has its roots in the k-nearest neighbors (k-NN) and self-organizing maps (SOM) clustering methods. Applications of these algorithms for extraction of rules from data, for control of complex systems with multiple operating modes, fault detection, and prognostics are presented in the chapter. (c) John Willey and Sons

Item Type: Contribution in Book/Report/Proceedings
Uncontrolled Keywords: /dk/atira/pure/researchoutput/libraryofcongress/qa75
Departments: Faculty of Science and Technology > School of Computing & Communications
ID Code: 919
Deposited By: Dr. Plamen Angelov
Deposited On: 24 Jan 2008 09:28
Refereed?: No
Published?: Published
Last Modified: 07 Jan 2020 02:18

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