Data mining is the process of extracting hidden patterns from huge data. Among the various clustering algorithms, k-means is the one of most widely used clustering technique in data mining. The performance of k-means clustering depends on the initial clusters and might converge to local optimum. K-means does not guarantee the unique clustering because it generates different results with randoml...

There exist techniques for decision making under speci c types of uncertainty, such as probabilistic, fuzzy, etc. Each of the corresponding ways of describing uncertainty has its advantages and limitations. As a result, new techniques for describing uncertainty appear all the time. Instead of trying to extend the existing decision making idea to each of these new techniques one by one, we attem...

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This letter derives a new interpretation for a family of competitive learning algorithms and investigates their relationship to fuzzy c-means and fuzzy learning vector quantization. These algorithms map a set of feature vectors into a set of prototypes associated with a competitive network that performs unsupervised learning. Derivation of the new algorithms is accomplished by minimizing an ave...

Type-2 (T2) fuzzy variable is an extension of an ordinary fuzzy variable. In fuzzy possibility theory, T2 fuzzy variable is defined as a measurable map from the universe to the set of real numbers, and the possibility of a T2 fuzzy variable takes on a real number is a regular fuzzy variable (RFV). T2 fuzziness, which is usually used to handle linguistic uncertainties, can be described as T2 fuz...

The concepts of fuzzy source and fuzzy successor operators for an L-fuzzy automaton (L is a latticeordered monoid) are introduced, which turn out to be L-fuzzy closure operators. When L is a quantale, these operators introduce L-fuzzy topologies. These observations are then used to give topological characterization of the separatedness and connectedness properties of an L-fuzzy automaton. c ©20...

This work explores the applicability of fuzzy clustering methods to the segmentation of sea surface temperature (SST) images for the automatic identification of upwelling areas in the coastal ocean of Portugal. This has been done by exploring the fuzzy c-means algorithm. Visualization of fuzzy c-partitions is achieved by means of color mapping. Selection of the best c-partition that represents ...

Suppressed fuzzy c-means (S-FCM) clustering algorithm with the intention of combining the higher speed of hard c-means clustering algorithm and the better classification performance of fuzzy c-means clustering algorithm had been studied by many researchers and applied in many fields. In the algorithm, how to select the suppressed rate is a key step. In this paper, we give a method to select the...

This paper presents a short overview of methods for fuzzy clustering and states desired properties for an optimal fuzzy document clustering algorithm. Based on these criteria we chose one of the fuzzy clustering most prominent methods – the c-means, more precisely probabilistic c-means. This algorithm is presented in more detail along with some empirical results of the clustering of 2-dimension...

This paper presents a fuzzy clustering algorithm, called the alternative fuzzy c-numbers (AFCN) clustering algorithm, for LR-type fuzzy numbers based on an exponential-type distance function. On the basis of the gross error sensitivity and in7uence function, this exponential-type distance is claimed to be robust with respect to noise and outliers. Hence, the AFCN clustering algorithm is more ro...