The purpose of this paper is to improve the performance of the kernel fuzzy clustering model by introducing a self-organized algorithm. A conventional kernel fuzzy clustering model is defined as a model which is an improved additive fuzzy clustering. The purpose of this conventional model is to obtain a clearer result by consideration of the interaction of clusters. This paper proposes a fuzzy ...

clustering is one of the known techniques in the field of data mining where data with similar properties is within the set of categories. k-means algorithm is one the simplest clustering algorithms which have disadvantages sensitive to initial values of the clusters and converging to the local optimum. in recent years, several algorithms are provided based on evolutionary algorithms for cluster...

Aim at the faults of Dynamic Clustering Algorithm based on Fuzzy Equation Matrix, we raise a fuzzy clustering algorithm based on factor analysis, which it combines the technology of reducing dimension using factor analyses method. The algorithm will deal with the sample collections before fuzzy clustering, which enlarge the scale of using dynamic clustering algorithm to resolve practical proble...

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...

The Gustafson-Kessel fuzzy clustering algorithm is capable of detecting hyperellipsoidal clusters of different sizes and orientations by adjusting the covariance matrix of data, thus overcoming the drawbacks of conventional fuzzy c-means algorithm. In this paper, an adaptive version of the Gustafson-Kessel algorithm is proposed. The way to adjust the covariance matrix iteratively is introduced ...

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...