We present a highly efficient, automatic method for the generation of hierarchical surface triangulations. Given a set of scattered points in three-dimensional space, without known connectivity information, our method reconstructs a valid triangulated surface model in a two-step procedure. First, we apply clustering to the set of given points and identify point subsets in locally nearly planar ...

Semantic relation clustering for unsupervised information extraction Most studies in unsupervised information extraction concentrate on the relation extraction and few work has been proposed on the organization of the extracted relations. We present in this paper a two-step clustering procedure to group semantically equivalent relations : a first step clusters relations with similar expressions...

Clustering is a crucial step in the analysis of gene expression data. Its goal is to identify the natural clusters and provide a reliable estimate of the number of distinct clusters in a given data set. In this paper we propose new hybrid algorithm for clustering of microarray data based on spectral clustering and k-means. Our algorithm consist of four steps, including preprocessing or filterin...

There are many studies about using traditional clustering algorithms like K-means, SOM and Two-Step algorithms to cluster electricity consumption data for definition of representative consumption patterns or for further classification and prediction work. However, these approaches are lack of scalability with high dimensions. Nevertheless, they are widely used, because algorithms for clustering...

this study aimed to compare some of the most important metacognitive belifes and thought control strategies among students with normal and pathological worry.the sample consisted of 146 undergratuate students selected by multiple clustering method.all subjects were asked to complete metacognitive belifes questionaire ( mcq(, penn state worry questionaure(pswq) and thought control strategies ( t...

Sparse representation and cooperative learning are two representative technologies in the field of multi-view spectral clustering. The former can effectively extract features multiple views by removal redundant information contained each view. latter incorporate diversity However, traditional sparse algorithms inadequate preserving internal geometric data manifold regularization. In fact, gener...

We present an automatic method for the generation of surface triangulations from sets of scattered points. Given a set of scattered points in three-dimensional space, without connectivity information, our method reconstructs a triangulated surface model in a two-step procedure. First, we apply an adaptive clustering technique to the given set of points, identifying point subsets in regions that...

Microarray experiments generate a considerable amount of data. Analyzing those data properly would help us gain a huge amount of biologically relevant information about the global cellular behavior. Clustering is one of the first steps in data analysis of high-throughput expression measurements. Many clustering algorithms have proved useful to make sense of such data. These algorithms, though u...