The key point in design of radial basis function networks is to specify the number and the locations of the centers. Several heuristic hybrid learning methods, which apply a clustering algorithm for locating the centers and subsequently a linear leastsquares method for the linear weights, have been previously suggested. These hybrid methods can be put into two groups, which will be called as in...

It has recently been shown [Weber, T. C. et al. (2007). "Acoustic propagation through clustered bubble clouds," IEEE J. Ocean. Eng. 32, 513-523] that gas bubble clustering plays a role in determining the acoustic field characteristics of bubbly fluids. In particular, it has been shown that clustering changes the bubble-induced attenuation as well as the ping-to-ping variability in the acoustic ...

The division of an image into meaningful structures, image segmentation, is often an essential step in image analysis, object representation, visualization, and many other image processing tasks. In this paper the multi-spectral satellite image is taken as input. Our aim is to enhance the accuracy and to remove the over segmentation..Mean-shift technique have been demonstrated to be capable of ...

We consider the problem of clustering data points in high dimensions, i.e., when the number of data points may be much smaller than the number of dimensions. Specifically, we consider a Gaussian mixture model (GMM) with two non-spherical Gaussian components, where the clusters are distinguished by only a few relevant dimensions. The method we propose is a combination of a recent approach for le...

Clustering is an unsupervised learning method that constitutes a cornerstone of an intelligent data analysis process. It is used for the exploration of inter-relationships among a collection of patterns, by organizing them into homogeneous clusters. Clustering has been dynamically applied to a variety of tasks in the field of Information Retrieval (IR). Clustering has become one of the most act...

Data objects with mixed numeric and categorical attributes are commonly encountered in real world. The k-prototypes algorithm is one of the principal algorithms for clustering this type of data objects. In this paper, we propose an improved k-prototypes algorithm to cluster mixed data. In our method, we first introduce the concept of the distribution centroid for representing the prototype of c...