Fuzzy clustering gives, in addition to the group membership for each object, a probability for belonging to one of the found clusters also for this method the number of clusters has to be defined in advance. [Pg.294]

Fuzzy clustering methods that have recently become popular are distinct from traditional clustering techniques in that molecules are permitted to belong to multiple clusters or have fractional membership in all clusters. A potential advantage of such classification schemes is that more than one similarity relationship can be established by cluster analysis. [Pg.13]

FIGURE 6.13 Fuzzy clustering uses membership coefficients to assign each object with varying probabilities to all k clusters. [Pg.280]

Kavuri, S. N., and Venkatasubramanian, V., Using fuzzy clustering with ellipsoidal units in neural networks for robust fault classification, Comput. Chem. Eng. 17(8), 765 (1993). [Pg.99]

Comput. Sci., 36 (6), 1195 (1996). Algorithm5 A Technique for Fuzzy Clustering of Chemical Inventories. [Pg.37]

Technische Universitat Wien Does latent class analysis, short-time Fourier transform, fuzzy clustering, support vector machines, shortest path computation, bagged clustering, naive Bayes classifier, etc. (http //cran.r-project.org/ web/packages/el071/index.html) [Pg.24]

Rassokhin, D., Lobanov, V. S., and Agrafiotis, D. K. (2000) Nonlinear mapping of massive data sets by fuzzy clustering and neural networks../. Comput. Chem. 21, 1-14. [Pg.49]

The partition coefficient is a measure of the quality of a fuzzy partition. The closer C(P) is to 1, the better the fuzzy partition P will be. The outputs of a fuzzy clustering algorithm for several different values of n may be compared by means of the partition coefficient. The best partition (and the best n) is that associated with the highest partition coefficient value. [Pg.338]

FIGURE 6.14 Example with two groups connected by a bridge of some objects (left) and resulting membership coefficients from fuzzy clustering for the left-hand side cluster. [Pg.281]

All these methods and the methods of the preceding section have one characteristic in common an object may be part of only one cluster. Fuzzy clustering applies other principles. It permits objects to be part of more than one cluster. This leads to results such as those illustrated by Fig. 30.16. Each object i is given a value [Pg.80]

New developments which have still to be checked for their usability in data evaluation of depth profiles are artificial neural networks [2.16, 2.21-2.25], fuzzy clustering [2.26, 2.27] and genetic algorithms [2.28]. [Pg.21]

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