Title

A Spectroscopy of Texts for Effective Clustering

Publication Type

Conference Proceeding Article

Publication Date

9-2004

Abstract

For many clustering algorithms, such as k-means, EM, and CLOPE, there is usually a requirement to set some parameters. Often, these parameters directly or indirectly control the number of clusters to return. In the presence of different data characteristics and analysis contexts, it is often difficult for the user to estimate the number of clusters in the data set. This is especially true in text collections such as Web documents, images or biological data. The fundamental question this paper addresses is: ldquoHow can we effectively estimate the natural number of clusters in a given text collection?rdquo. We propose to use spectral analysis, which analyzes the eigenvalues (not eigenvectors) of the collection, as the solution to the above. We first present the relationship between a text collection and its underlying spectra. We then show how the answer to this question enhances the clustering process. Finally, we conclude with empirical results and related work.

Discipline

Databases and Information Systems | Numerical Analysis and Scientific Computing

Research Areas

Data Management and Analytics

Publication

Knowledge Discovery in Databases: PKDD 2004: 8th European Conference on Principles and Practice of Knowledge Discovery in Databases, Pisa, Italy, September 20-24: Proceedings

Volume

3202

First Page

301

Last Page

312

ISBN

9783540301165

Identifier

10.1007/978-3-540-30116-5_29

Publisher

Springer Verlag

City or Country

Pisa, Italy

Additional URL

http://dx.doi.org/10.1007/978-3-540-30116-5_29