Recently, more and more researchers have been paying close attention to tumor image processing techniques, in which the tumor image segmentation technology is regarded as the absolute research focus. Given that there exist only one tumor image segmentation algorithm, no further high quality three-dimensional slice can be provided for the later reconstruction of the three-dimensional reconstruct...

Nowadays, the Fuzzy C-Means method has become one of the most popular clustering methods based on minimization of a criterion function. However, the performance of this clustering algorithm may be significantly degraded in the presence of noise. This paper presents a robust clustering algorithm called Bilateral Weighted Fuzzy CMeans (BWFCM). We used a new objective function that uses some kinds...

Image segmentation plays an important role in image analysis. It is one of the first and most important tasks in image analysis and computer vision. This proposed system presents a variation of fuzzy cmeans algorithm that provides image clustering. Based on the Mercer kernel, the kernel fuzzy c-means clustering algorithm (KFCM) is derived from the fuzzy c-means clustering algorithm (FCM).The KF...

Fuzzy co-clustering is a method that performs simultaneous fuzzy clustering of objects and features. In this paper, we introduce a new fuzzy coclustering algorithm for high-dimensional datasets called Cosine-Distancebased & Dual-partitioning Fuzzy Co-clustering (CODIALING FCC). Unlike many existing fuzzy co-clustering algorithms, CODIALING FCC is a dualpartitioning algorithm. It clusters the fe...

A type-2 Takagi-Sugeno-Kang fuzzy neural system is proposed and its parameter update rules are derived using fuzzy clustering and gradient learning algorithms. The proposed type-2 fuzzy neural system is used for the control and the identification of a real-time servo system. Fuzzy c-means clustering algorithm is used to determine the initial places of the membership functions to ensure that the...

In medical imaging uncertainty is widely present in data, because of the noise in acquisition and of the partial volume effects originating from the low resolution of sensors. In particular, borders between tissues are not exactly defined and memberships in the boundary regions are intrinsically fuzzy. Therefore, computer assisted unsupervised fuzzy clustering methods turn out to be particularl...

MOTIVATION It is well understood that the successful clustering of expression profiles give beneficial ideas to understand the functions of uncharacterized genes. In order to realize such a successful clustering, we investigate a clustering method based on adaptive resonance theory (ART) in this report. RESULTS We apply Fuzzy ART as a clustering method for analyzing the time series expression...

In this paper the conventional fuzzy k-modes algorithm for clustering categorical data is extended by representing the clusters of categorical data with fuzzy centroids instead of the hard-type centroids used in the original algorithm. Use of fuzzy centroids makes it possible to fully exploit the power of fuzzy sets in representing the uncertainty in the classification of categorical data. To t...

Fuzzy C-Mean (FCM) is an unsupervised clustering algorithm based on fuzzy set theory that allows an element to belong to more than one cluster. Where fuzzy means “unclear” or “not defined” and c denotes “clustering”. In FCM the number of cluster are randomly selected. [15] FCM is the advanced version of K-means clustering algorithm and doing more work than K-means. K-Means just needs to do a di...

Missing values in datasets should be extracted from the datasets or should be estimated before they are used for classification, association rules or clustering in the preprocessing stage of data mining. In this study, we utilize a fuzzy c-means clustering hybrid approach that combines support vector regression and a genetic algorithm. In this method, the fuzzy clustering parameters, cluster si...