Segmentation of brain tissues is one important process prior to many analysis and visualization tasks for magnetic resonance (MR) images. Clustering is one of the unsupervised techniques for doing the segmentation. Clustering is done with probabilistic, possibilistic and plausibilistic approaches. Most of segmentation techniques have relied on multi channel characteristics of MR images while a ...

Clustering and tessellations are basic tools in data mining. The k-means and EM algorithms are two of the most important algorithms in the Mixture Model-based clustering and tessellations. In this paper, we introduce a new clustering strategy which shares common features with both the EM and k-means algorithms. Our methods also lead to more general tessellations of a spatial region with respect...

The algorithms proposed to date for categorizing WEB-visitors are all of quadratic time complexity (they essentially require computing the dissimilarity between all pairs of paths). These clustering eeorts, although not scalable, have demonstrated the extensive beneets and sophisticated applications emerging from identifying groups of visitors to a web site. We provide a sub-quadratic clusterin...

The ability to cluster data accurately is essential to applications such as image segmentation. Therefore, techniques that enhance accuracy are of keen interest. One such technique involves applying a quantum mechanical system model, such as that of the quantum bit, to generate probabilistic numerical output to be used as variable input for clustering algorithms. This work demonstrates that app...

Swarm Intelligence (SI) is an innovative artificial intelligence technique for solving complex optimization problems. Data clustering is the process of grouping data into a number of clusters. The goal of data clustering is to make the data in the same cluster share a high degree of similarity while being very dissimilar to data from other clusters. Clustering algorithms have been applied to a ...

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...

Recently, some statistical studies have been done using the shape data. One of these studies is clustering shape data, which is the main topic of this paper. We are going to study some clustering algorithms on shape data and then introduce the best algorithm based on accuracy, speed, and scalability criteria. In addition, we propose a method for representing the shape data that facilitates and ...

Modeling and predicting co-occurrences of events is a fundamental problem of unsupervised learning. In this contribution, we develop a general statistical framework for analyzing co-occurrence data based on probabilistic clustering by mixture models. More specifically, we discuss three models which pursue different modeling goals and which differ in the way they define the probabilistic partiti...

This paper addresses the problem of clustering data when the available data measurements are not multivariate vectors of xed dimensionality. For example, one might have data from a set of medical patients, where for each patient there are time series, image, text, and multivariate data. We propose a general probabilistic clustering framework for clustering heterogeneous data types of this form....

clustering is the process of dividing a set of input data into a number of subgroups. the members of each subgroup are similar to each other but different from members of other subgroups. the genetic algorithm has enjoyed many applications in clustering data. one of these applications is the clustering of images. the problem with the earlier methods used in clustering images was in selecting in...