A scheme to develop the image over-segmentation task is introduced in this paper, it considers pixels of an as intuitive fuzzy sets and develops intuitionistic clustering process them. In regard, main contribution provide a method for extracting superpixels with greater adherence edges regions. Experimental tests were developed considering biomedical grayscale natural color images. The robustne...

The performance of level set segmentation is subjected to appropriate initialization and optimal configuration of controlling parameters which require substantial manual invention. A new spatial fuzzy level set algorithm is proposed in this paper to facilitate medical image segmentation. It is able to directly evolve from initial segmentation by spatial fuzzy clustering. The controlling variabl...

In this article, we address the problem of clustering imprecise data using finite mixtures of Gaussians. We propose to estimate the parameters of the mixture model using the fuzzy EM algorithm. This extension of the EM algorithm allows us to handle imprecise data represented by fuzzy numbers. First, we briefly recall the principle of the fuzzy EM algorithm. Then, we provide the update equations...

Classification refers to a set of methods that predict the class of an object from attributes or features describing the object. In this paper we present a fuzzy classification algorithm to predict bankruptcy. Our classification algorithm is modified from a subspace clustering algorithm called fuzzy subspace clustering (FSC). As our algorithm associates each feature of a class with a fuzzy memb...

This paper analyzes the suitability of fuzzy clustering methods for the discovery of relevant document relationships, motivated by the need for enhanced relevancebased navigation of Web-accessible resources. The performance evaluation of a modified Fuzzy c-Means algorithm is carried out, and a comparison with a traditional hard clustering technique is presented. Clustering precision and recall ...

In this paper, the fuzzy multi-depot vehicle routing problem with simultaneous pickup and delivery (FMDVRP-SPD) is investigated. The FMDVRP-SPD is the problem of allocating customers to several depots, so that the optimal set of routes is determined simultaneously to serve the pickup and the delivery demands of each customer within scattered depots. In the problem, both pickup and delivery dema...

This article explains how to apply the deterministic annealing (DA) and simulated annealing (SA) methods to fuzzy entropy based fuzzy c-means clustering. By regularizing the fuzzy c-means method with fuzzy entropy, a membership function similar to the Fermi-Dirac distribution function, well known in statistical mechanics, is obtained, and, while optimizing its parameters by SA, the minimum of t...

Clustering is an important research topic that has practical applications in many 5elds. It has been demonstrated that fuzzy clustering, using algorithms such as the fuzzy C-means (FCM), has clear advantages over crisp and probabilistic clustering methods. Like most clustering algorithms, however, FCM and its derivatives need the number of clusters in the given data set as one of their initiali...