In this paper, we propose a new document clustering method based on the K-means method (kmeans). In our method, we allow only finite candidate vectors to be representative vectors of kmeans. We also propose a method for constructing these candidate vectors using documents that have the same word. We participated in NTCIR-4 WEB Task D (Topic Classification Task) and experimentally compared our m...

This study presents a position model for evaluating the image of tourists a destination. The evaluation is based on secondary data from 1999 through 2004, using a database composed of 20,023 respondents. Data are analyzed using the K-Means data mining method. Analytical results indicate that the destination image position (DIP) model is established, and four groups of visitor are identified. Th...

Clustering, in particular k-means clustering, is a central topic in data analysis. Clustering with Bregman divergences is a recently proposed generalization of k-means clustering which has already been widely used in applications. In this paper we analyze theoretical properties of Bregman clustering when the number of the clusters k is large. We establish quantization rates and describe the lim...

One way of representing semantics could be via a high dimensional conceptual space constructed by certain lexical semantic space models. Concepts (words), represented as a vector of other words in the semantic space, can be categorized via clustering techniques into a number of regions reflecting different contexts. The conventional clustering algorithms, e.g., K-means method, however, normally...

The issue of determining “the right number of clusters” in K-Means has attracted considerable interest, especially in the recent years. Cluster overlap appears to be a factor most affecting the clustering results. This paper proposes an experimental setting for comparison of different approaches at data generated from Gaussian clusters with the controlled parameters of betweenand within-cluster...

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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...