Principles of data mining
Pattern Recognition Algorithms for Data Mining: Scalability, Knowledge Discovery, and Soft Granular Computing
Nonparametric estimation for control engineering
CONTROL'08 Proceedings of the 4th WSEAS/IASME international conference on Dynamical systems and control
A complete gradient clustering algorithm formed with kernel estimators
International Journal of Applied Mathematics and Computer Science - Computational Intelligence in Modern Control Systems
Fuzzy controller for mechanical systems
IEEE Transactions on Fuzzy Systems
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At present, statistical kernel estimators constitute the dominant -- in practice -- method of nonparametric estimation. It allows the useful characterization of probability distributions without arbitrary assumptions regarding their membership to a fixed class. In this paper their use to the basic tasks of data analysis and exploration, i.e. identification of outliers, clustering, and classification, will be considered. In every case the final result will be an algorithm ensuring that its practical implementation does not demand of the user detailed knowledge of the theoretical aspects, or laborious research and calculations. The above presented theory has been successfully applied to various practical problems of engineering and management. Two of these, the design of a fault detection and diagnosis system for automatic control purposes, and a marketing support strategy for a mobile phone operator, will be demonstrated in detail. Useful procedures for the reduction of dimensionality and size of a random sample, subordinated to the specificity of kernel estimators, will also be commented.