This work proposes a simple way to improve a clustering algorithm. The idea is to exploit a new distance metric called the “Euclidian Commute Time” (ECT) distance, based on a random walk model on a graph derived from the data. Using this distance measure instead of the usual Euclidean distance in a k-means algorithm allows to retrieve wellseparated clusters of arbitrary shape, without working h...

Numerous research studies have explored the effect of hypermedia on learners’ performance using Web Based Instruction (WBI). A learner’s performance is determined by their varying skills and abilities as well as various differences such as gender, cognitive style and prior knowledge. In this paper, we investigate how differences between individuals influenced learner’s performance using a hyper...

a combination of efficiency, emissions, noise levels, and other criteria. Researchers routinely classify documents as “relevant to the current project” or “irrelevant.” Genome decoding divides chromosomes into genes, regulatory regions, signals, and so on. Pathologists identify cells as cancerous or benign. We can classify data into different groups by clustering data that are close with respec...

Clustering allows us to extract groups of genes that are tightly coexpressed from Microarray data. In this paper, a new method DSF_Clust is developed to find dominant sets (clusters). We have preformed DSF_Clust on several gene expression datasets and given the evaluation with some criteria. The results showed that this approach could cluster dominant sets of good quality compared to kmeans met...

A novel method based on Wikipedia for clustering keyword of reviews is proposed. Users can quickly finding the themes they interest through it. First the method extracts keywords, then calculates word similarity based on Wikipedia to generate similarity matrix, finally uses k-means to cluster. The performance is better than the methods which based on How-net and Word-net. The accuracy is around...

Thirdand fourth-order accurate finite difference schemes for the first derivative of the square of the speed are developed, for both uniform and non-uniform grids, and applied in the study of a two-dimensional viscous fluid flow through an irregular domain. The von Mises transformation is used to transform the governing equations, and map the irregular domain onto a rectangular computational do...

Data clustering is an approach to seek for structure in sets of complex data, i.e., sets of “objects”. The main objective is to identify groups of objects which are similar to each other, e.g., for classification. Here, an introduction to clustering is given and three basic approaches are introduced: the k-means algorithm, neighbour-based clustering, and an agglomerative clustering method. For ...

In K-means clustering, we are given a set of n data points in multidimensional space, and the problem is to determine the number k of clusters. In this paper, we present three methods which are used to determine the true number of spherical Gaussian clusters with additional noise features. Our algorithms take into account the structure of Gaussian data sets and the initial centroids. These thre...

This paper presents a personalized long-term electrocardiogram (ECG) classification framework, which addresses the problem within a long-term ECG signal, known as Holter register, recorded from an individual patient. Due to the massive amount of ECG beats in a Holter register, visual inspection is quite difficult and cumbersome, if not impossible. Therefore, the proposed system helps profession...

This paper considers the use of clustering techniques to learn the mobility patterns existing in a cellular network. These patterns are materialized in a database of prototype trajectories obtained after having observed multiple trajectories of mobile users. Both K-means and Self-Organizing Maps (SOM) techniques are assessed. Different applicability areas in the context of SelfOrganizing Networ...