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Cluster Analysis for Data Mining and System Identification

✍ Scribed by Janos Abonyi, Balazs Feil


Publisher
Birkhäuser Basel
Year
2007
Tongue
English
Leaves
323
Edition
1
Category
Library

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✦ Synopsis


The aim of this book is to illustrate that advanced fuzzy clustering algorithms can be used not only for partitioning of the data. It can also 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. This book is oriented to undergraduate and postgraduate and is well suited for teaching purposes.


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Cluster Analysis for Data Mining and Sys
✍ János Abonyi, Balázs Feil 📂 Library 📅 2007 🏛 Birkhäuser Basel 🌐 English

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✍ János Abonyi, Balázs Feil 📂 Library 📅 2007 🏛 Birkhäuser Basel 🌐 English

<P>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 i

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✍ János Abonyi, Balázs Feil 📂 Library 📅 2007 🏛 Birkhäuser Basel 🌐 English

<P>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 i

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✍ János Abonyi, Balázs Feil 📂 Library 📅 2007 🏛 Birkhäuser Basel 🌐 English

<P>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 i

Cluster Analysis for Data Mining and Sys
✍ János Abonyi, Balázs Feil 📂 Library 📅 2007 🏛 Birkhäuser 🌐 English

<P>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 i