Cluster Analysis In Document Networks
Author(s)
C. K. dos Santos, A. G. Evsukoff & B. S. L. P. de Lima
Abstract
Text or document clustering is a subset of a larger field of data clustering and has
been one of the research hotspots in text mining. On the other hand, recent
studies have shown that many real systems may be represented as complex
networks with astonishing similar proprieties. In this work a document corpora is
represented as a complex network of documents, in which the nodes represent
the documents and the edges are weighted according to the similarities among
documents. The detection of community structures in complex networks can be
seen as the cluster analysis in document networks. Recently community
detection algorithms based on spectral proprieties of the underlying has shown
good results. The main motivation for applying those methods is that they have
shown to be robust to the high dimensionality of feature space and also to the
inherent data sparsity resulting from text representation in the vector space
model. The aim of this paper is to present the application of the community
structures algorithms for text mining. Experiments have been carried out on the
document clustering problems taken from 20 newsgroup document corpora to
evaluate the performance of the proposed approach.
Keywords: text mining, document clustering, complex networks, community
detection, spectral clustering.
1 Introduction
Unstructured information in document databases presents intrinsic characteristics
such that the classical data mining algorithms can be adapted to solve text
mining tasks. One of the most usual representations for text mining relies on the
vector space information retrieval model of documents [1]. In such a model the
order of words is not considered and each document in a collection is represented
by a vector, of which the components are related to relevant words appearing in
Keywords
text mining, document clustering, complex networks, community
detection, spectral clustering.
Related Book
Other papers in this volume
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