In this paper an optimized method for unsupervised image clustering is proposed. Generally a Novel Fuzzy C Means (FCM) or FCM based clustering algorithm are used for clustering based image segmentation but these algorithms have a disadvantage of depending upon supervised user inputs such as number of clusters. Our proposed algorithm enhances an unsupervised preliminary process known as Double C...

In daily life environmental sounds that are present around us include intricately mixed sounds emitted from different sources. This work aim is to contribute a method for sound source separation by means of the enhanced k-means clustering with adaptive genetic algorithm. At first removes the features from the input audio signal by means of Mel Frequency Cepstral Coefficients (MFCC) and spectral...

Aiming at the degeneration phenomenon of images taken in mist, according to the features of the degraded images, an improved haze removal algorithm based on genetic fuzzy clustering is presented in this paper after analyzing its defects and shortcomings. Firstly, the improved atmospheric scattering model is established. Secondly, the original image is converted into a standard image through the...

Some of the well-known fuzzy clustering algorithms are based on Euclidean distance function, which can only be used to detect spherical structural clusters. Gustafson-Kessel clustering algorithm and Gath-Geva clustering algorithm were developed to detect non-spherical structural clusters. However, the former needs added constraint of fuzzy covariance matrix, the later can only be used for the d...

This paper presents a segmentation method for line extraction in 2-D range images. It uses a prototype-based fuzzy clustering algorithm in a split-and-merge framework. The split-and-merge structure allows us to use the fuzzy clustering algorithm without any previous knowledge on the number of prototypes. This algorithm aims to be used in mobile robots navigation systems for dynamic map building...

A systematic approach for constructing interpretable fuzzy model based on reduction methodology is proposed. Fuzzy clustering algorithm, combined with least square method, is used to identify initial fuzzy model with overestimated rule number. Orthogonal least square algorithm and similar fuzzy sets merging are then applied to remove redundancy of the fuzzy model. In order to obtain high accura...

In this paper, we derive a new application of fuzzy systems designed for a generalized autoregression conditional heteroscedasticity (GARCH) model. In general, stock market performance is time-varying and nonlinear, and exhibits properties of clustering. The latter means simply that certain large changes tend to follow other large changes, and in general small changes tend to follow other small...