Medical image preprocessing and segmentation are very important in medical area. This paper deal with the implementation of simple algorithm for detection of skin cancer also and infected area. Skin cancer are the most common cancer in human. Skin cancer are curable cancer after early detection. In hospital doctor used a biopsy (a laboratory medical procedure). There are many different type of ...

A simple and effective fuzzy clustering approach is presented for fuzzy modeling from industrial data. In this approach, fuzzy clustering is implemented in two phases: data compression by a self-organizing network, and fuzzy partitioning via fuzzy cmeans clustering associated with a proposed cluster validity measure. The approach is used to extract fuzzy models from data and find out the optima...

A conventional fuzzy cmeans (FCM) clustering algorithm did not use the spatial information of the data and is very much sensitive to noise. To improve the noise sensitivity of FCM, Spatial FCM (SFCM) incorporates the spatial information to improve the results. Intuitionistic fuzzy sets introduce hesitation factor in the fuzzy sets to enhance the performance of fuzzy sets and also added entropy ...

The most widely used clustering algorithm implementing the fuzzy philosophy is Fuzzy CMeans (FCM) .In this paper, we have proposed a new Hybrid FCM with Genetic Algorithm (GA), we get an improved FCM algorithm which has not only the global search capability of GA but also the local search capability of FCM, and hence can better solve the clustering problem. An improved version of this hybrid cl...

Image segmentation is the most practical approach among all virtually automated image recognition systems. Feature extraction and recognition have numerous applications on telecommunication, weather forecasting, environment exploration and medical diagnosis. This paper deals with different image segmentation algorithms. The quality of satellite image is affected by atmosphere, temperature etc. ...

A novel high-order fuzzy time series model for stock price forecasting is presented based on the fuzzy cmeans (FCM) discretization method and artificial neural networks (ANN). In the proposed model, the FCM discretization method obtained reliable interval lengths. In addition, the fuzzy relation matrix was obtained from ANN, mooting the need for complex and time-consuming matrix operations. The...

In this paper an autonomous feature clustering framework has been proposed for performance and reliability evaluation of an environmental sensor network. Environmental time series were statistically preprocessed to extract multiple semantic features. A novel hybrid clustering framework was designed based on Principal Component Analysis (PCA), Guided Self-Organizing Map (G-SOM), and Fuzzy-CMeans...

An improved initialization method for fuzzy cmeans (FCM) method is proposed which aims at solving the two important issues of clustering performance affected by initial cluster centers and number of clusters. A density based approach is needed to identify the closeness of the data points and to extract cluster center. DBSCAN approach defines ε–neighborhood of a point to determine the core objec...

A fuzzy clustering algorithm for intrusion detection based on heterogeneous attributes is proposed in this paper. Firstly, the algorithm modifies the comparability measurement for the categorical attributes according to the formula of Hemingway; then, for the shortages of fuzzy Cmeans clustering algorithm: initialize sensitively and easy to get into the local optimum, the presented new algorith...