Dataclusteringisacommontechniqueforstatisticaldataanalysis, whichisusedin many ?elds, including machine learning, data mining, pattern recognition, image analysis and bioinformatics. Clustering is the classi?cation of similar objects into di?erent 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 de?ned 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...
Dataclusteringisacommontechniqueforstatisticaldataanalysis, whichisusedin many ?elds, including machine learning, data mining, pattern recognition, im...
This work presents a data visualization technique that combines graph-based topology representation and dimensionality reduction methods to visualize the intrinsic data structure in a low-dimensional vector space. The application of graphs in clustering and visualization has several advantages. A graph of important edges (where edges characterize relations and weights represent similarities or distances) provides a compact representation of the entire complex data set. This text describes clustering and visualization methods that are able to utilize information hidden in these graphs, based...
This work presents a data visualization technique that combines graph-based topology representation and dimensionality reduction methods to visualize ...
This book presents new approaches to constructing fuzzy models for model-based control. Simulated examples and real-world applications from chemical and process engineering illustrate the main methods and techniques. Supporting MATLAB and Simulink files create a computational platform for exploration of the concepts and algorithms.
This book presents new approaches to constructing fuzzy models for model-based control. Simulated examples and real-world applications from chemica...
The key idea of this book is that hinging hyperplanes, neural networks and support vector machines can be transformed into fuzzy models, and interpretability of the resulting rule-based systems can be ensured by special model reduction and visualization techniques. The first part of the book deals with the identification of hinging hyperplane-based regression trees. The next part deals with the validation, visualization and structural reduction of neural networks based on the transformation of the hidden layer of the network into an additive fuzzy rule base system. Finally, based on the...
The key idea of this book is that hinging hyperplanes, neural networks and support vector machines can be transformed into fuzzy models, and interp...
