As organizations accumulate data over time, the problem of tracking how patterns evolve becomes important. In this paper, we present an algorithm to track the evolution of cluster...
In this paper we present our technique for finding semantically similar clusters within web documents obtained from a set of queries retrieved from the Google search engine. This ...
Abstract. We applied different clustering algorithms to the task of clustering multi-word terms in order to reflect a humanly built ontology. Clustering was done without the usual ...
This paper discusses two problems that arise in the Generation of Referring Expressions: (a) numeric-valued attributes, such as size or location; (b) perspective-taking in referen...
Common document clustering algorithms utilize models that either divide a corpus into smaller clusters or gather individual documents into clusters. Hierarchical Agglomerative Clus...
The identification of categories in image databases usually relies on clustering algorithms that only exploit the feature-based similarities between images. The addition of semant...
Abstract. The starting point of this work is the definition of local pattern detection given in [10] as the unsupervised detection of local regions with anomalously high data densi...
In the past decades, many clustering algorithms have been proposed for the analysis of gene expression data, but little guidance is available to help choose among them. Given the ...
Huge amount of gene expression data have been generated as a result of the human genomic project. Clustering has been used extensively in mining these gene expression data to find...
Several researchers have illustrated that constraints can improve the results of a variety of clustering algorithms. However, there can be a large variation in this improvement, e...