Cluster Analysis for Data Mining and System Identification
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Beschreibung
This book presents new approaches to data mining and system identification. Algorithms that can be used for the clustering of data have been overviewed. New techniques and tools are presented for the clustering, classification, regression and visualization of complex datasets. Special attention is given to the analysis of historical process data, tailored algorithms are presented for the data driven modeling of dynamical systems, determining the model order of nonlinear input-output black box models, and the segmentation of multivariate time-series. The main methods and techniques are illustrated through several simulated and real-world applications from data mining and process engineering practice.
The book is aimed primarily at practitioners, researches, and professionals in statistics, data mining, business intelligence, and systems engineering, but it is also accessible to graduate and undergraduate students in applied mathematics, computer science, electrical and process engineering. Familiarity with the basics of system identification and fuzzy systems is helpful but not required.
Key features:
- Detailed overview of the most powerful algorithms and approaches for data mining and system identification is presented.
- Extensive references give a good overview of the current state of the application of computational intelligence in data mining and system identification, and suggest further reading for additional research.
- Numerous illustrations to facilitate the understanding of ideas and methods presented.
- Supporting MATLAB files, available at the website www.fmt.uni-pannon.hu/softcomp create a computational platform for exploration and illustration of many concepts and algorithms presented in the book.
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.
Detailed overview of the most powerful algortihms and approaches for data mining and system identification is presented Extensive references give a good overview of the current state of the application of computational intelligence in data mining and system identification, and suggest further reading for additional research Numerous illustrations to facilitate the understanding of ideas and methods presented Supporting MATLAB files, available at the website www.fmt.uni-pannon.hu/softcomp create a computational platform for exploration and illustration of many concepts and algorithms presented in the book Includes supplementary material: sn.pub/extras
Autor*in
János Abonyi
Themen in »Cluster Analysis for Data Mining and System Identification«
Cluster Analysis Clustering Computer Data Mining Fuzzy algorithms calculus classification clustering algorithm computer science dynamische Systeme linear optimization modeling process engineering statistics