In this paper, the well-known k-means algorithm for searching for a locally 12 optimal partition of the setA ⊂ R is analyzed in the case if some data points occur on the 13 border of two or more clusters. For this special case, a useful strategy by implementation 14 of the k-means algorithm is proposed. 15

Abstract. An application of cluster analysis to identify topics in a collection of posters abstracts from the Society for Neuroscience (SfN) Annual Meeting in 2006 is presented. The topics were identified by selecting from the abstracts belonging to each cluster the terms with the highest scores using different ranking schemes. The ranking scheme based on logentropy showed better performance in...

Using a corpus of over 17,000 financial news reports (involving over 10M words), we perform an analysis of the argument-distributions of the UPand DOWN-verbs used to describe movements of indices, stocks, and shares. Using measures of the overlap in the argument distributions of these verbs and k-means clustering of their distributions, we advance evidence for the proposal that the metaphors re...

One of the most widely used clustering techniques is the kmeans algorithm. Solutions obtained from this technique are dependent on the initialisation of cluster centres. In this article, two initialisation methods are developed. These methods are particularly suited to problems involving very large data sets. The methods have been applied to di erent data sets and good results are obtained.

Much of the challenge and appeal in remixing music comes from manipulating samples. Typically, identifying distinct samples of a song requires expertise in music production software. Additionally, it is di cult to visualize similarities and di↵erences between all samples of a song simultaneously and use this to select samples. MusicMapper is a web application that allows nonexpert users to find...

This paper proposed improved K-means clustering algorithm based on user tag. It first used social annotation data to expand the vector space model of K-means. Then, it applied the links involved in social tagging network to enhance the clustering performance. Experimental result shows that the proposed improved K-means clustering algorithm based on user tag is effective.

It is well-known that dimX ≤ n if and only if every map of a closed subspace of X into the n-dimensional sphere Sn can be extended over X. It is also well-known that for the cohomological dimension dimGX of X with respect to an abelian coefficient group G, dimGX ≤ n if and only if every map of a closed subspace of X into the Eilenberg-Mac Lane complex K(G,n) extends over X. These properties giv...

Human action recognition is an important yet challenging task. In this paper, a simple and efficient method based on random forests is proposed for human action recognition. First, we extract the 3D skeletal joint locations from depth images. The APJ3D computed from the action depth image sequences by employing the 3D joint position features and the 3D joint angle features, and then clustered i...