Abonyi J., Feil B. Cluster Analysis for Data Mining and System Identification

Blackwell, 2007. — 303 p. — ISBN: 978-3-7643-7987-2.Data clustering is a common technique for statistical data analysis, which is used in many fields, including machine learning, data mining, pattern recognition, image analysis and bioinformatics. Clustering is the classification of similar objects into different groups, or more precisely, the partitioning of a data set into subsets (clusters), so that the data in each subset (ideally) share some common trait – often proximity according to some defined distance measure.
The aim of this book is to illustrate that advanced fuzzy clustering algorithms can be used not only for partitioning of the data, but it can be used for visualization, regression, classification and time-series analysis, hence fuzzy cluster analysis is a good approach to solve complex data mining and system identification problems.Classical Fuzzy Cluster Analysis
Visualization of the Clustering Results
Clustering for Fuzzy Model Identification – Regression
Fuzzy Clustering for System Identification
Fuzzy Model based Classifiers
Segmentation of Multivariate Time-series
Hermite Spline Interpolation