A method for the initialisation step of clustering algorithms is presented. It is based on the concept of cluster as a high density region of points. The search space is modelled as a set of d-dimensional cells. A sample of points is chosen and located into the appropriate cells. Cells are iteratively split as the number of points they receive increases. The regions of the search space having a...

We study the problem of estimating a manifold from random samples. In particular, we consider piecewise constant and piecewise linear estimators induced by k-means and k-flats, and analyze their performance. We extend previous results for k-means in two separate directions. First, we provide new results for k-means reconstruction on manifolds and, secondly, we prove reconstruction bounds for hi...

Area of classifying satellite imagery has become a challenging task in current era where there is tremendous growth in settlement i.e. construction of buildings, roads, bridges, dam etc. This paper suggests an improvised k-means and Artificial Neural Network (ANN) classifier for land-cover mapping of Eastern Himalayan state Sikkim. The improvised k-means algorithm shows satisfactory results com...

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...

.......................................................................................................2 Background .................................................................................................5 Stars and Spectroscopy with the Spitzer Space Telescope .................................5 Clustering Methodologies .....................................................................