Cluster analysis for data mining and system identification by János Abonyi, Balázs Feil

By János Abonyi, Balázs Feil

The goal of this booklet is to demonstrate that complex fuzzy clustering algorithms can be utilized not just for partitioning of the information. it could possibly even be used for visualisation, regression, category and time-series research, for this reason fuzzy cluster research is an effective method of remedy advanced facts mining and approach identity difficulties. This ebook is orientated to undergraduate and postgraduate and is easily suited to instructing purposes.

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Realization based on Alternative Optimization. The most popular technique is Alternative Optimization even today, maybe because of its simplicity [39, 79]. This technique will be presented in the following sections. 16) represents a nonlinear optimization problem that can be solved by using a variety of available methods, 20 Chapter 1. Classical Fuzzy Cluster Analysis ranging from grouped coordinate minimization, over simulated annealing to genetic algorithms. 16), known as the fuzzy c-means (FCM) algorithm.

We briefly discuss the first two methods in this subsection, and the last one in the next subsections with more details. 5. Fuzzy Clustering 19 • Realization based on NN. The application of neural networks in cluster analysis stems from the Kohonen’s learning vector quantization (LVQ) [156], Self Organizing Mapping (SOM) [157] and Grossberg’s adaptive resonance theory (ART) [59, 105, 106]. Since NNs are of capability in parallel processing, people hope to implement clustering at high speed with network structure.

Realization based on EC. EC is a stochastic search strategy with the mechanism of natural selection and group inheritance, which is constructed on the basis of biological evolution. For its performance of parallel search, it can obtain the global optima with a high probability. In addition, EC has some advantages such as it is simple, universal and robust. To achieve clustering results quickly and correctly, evolutionary computing was introduced to fuzzy clustering with a series of novel clustering algorithms based on EC (see the review of Xinbo et al in [286]).

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