Abonyi / Feil Cluster Analysis for Data Mining and System Identification
1. Auflage 2007
ISBN: 978-3-7643-7988-9
Verlag: Springer
Format: PDF
Kopierschutz: 1 - PDF Watermark
E-Book, Englisch, 306 Seiten, Web PDF
Reihe: Mathematics and Statistics
ISBN: 978-3-7643-7988-9
Verlag: Springer
Format: PDF
Kopierschutz: 1 - PDF Watermark
This book identifies fuzzy cluster analysis as a good approach to solving complex data mining and system identification problems. It illustrates how 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. 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.
Zielgruppe
Professional/practitioner
Autoren/Hrsg.
Weitere Infos & Material
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.
