A Multi-Criteria Document Clustering Method Based on Topic Modeling and Pseudoclosure Function

We address in this work the problem of document clustering. Our contribution proposes a novel unsupervised clustering method based on the structural analysis of the latent semantic space. Each document in the space is a vector of probabilities that represents a distribution of topics. The document m...

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Bibliographic Details
Published in:Informatica (Ljubljana) 2016-06, Vol.40 (2), p.169-169
Main Authors: Bui, Quang Vu, Sayadi, Karim, Bui, Marc
Format: Article
Language:English
Subjects:
Clustering
Clusters
Computation
Criteria
Mathematical analysis
Semantics
Structural analysis
Vectors (mathematics)
Online Access:Get full text
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Summary:We address in this work the problem of document clustering. Our contribution proposes a novel unsupervised clustering method based on the structural analysis of the latent semantic space. Each document in the space is a vector of probabilities that represents a distribution of topics. The document membership to a cluster is computed taking into account two criteria: the major topic in the document (qualitative criterion) and the distance measure between the vectors of probabilities (quantitative criterion). We perform a structural analysis on the latent semantic space using the Pretopology theory that allows us to investigate the role of the number of clusters and the chosen centroids, in the similarity between the computed clusters. We have applied our method to Twitter data and showed the accuracy of our results compared to a random choice number of clusters.
ISSN:0350-5596
1854-3871