We are not able to deal with a mammoth text corpus without summarizing them into a
relatively small subset. A computational tool is extremely needed to understand such a …

E-petitions have become a popular vehicle for political activism, but studying them has been
difficult because efficient methods for analyzing their content are currently lacking …

We propose a parallel generalization scheme for Singular Value Decomposition–based
clustering algorithms. The scheme enables the clustering algorithm to generate a hierarchy …

Abstract Probabilistic Latent Semantic Analysis (PLSA) is a fundamental text analysis
technique that models each word in a document as a sample from a mixture of topics. PLSA …

In this paper, it is proposed to identify the technological complementarity of enterprises. The
process of identifying potential partners is based on the comparison of cluster information …

A limited survey of ML techniques viewed from quality of output as implied by embedded
and explicit metrics (eg, optimization in LP, sensitivity analysis vs. possible binary …

Objectives. In our study, we provide an overview of the amount of research that has been
done in relation to topic models. We want to uncover various quality criteria, evaluation …

In this paper, k-means parallel clustering algorithm is studied. Firstly, this paper introduces
the purpose and significance of k-means clustering algorithm. Secondly, we describe the …

Cloud Computing is the development of Distributed Computing, Parallel Computing and
Grid Computing. Cloud computing is a new distributed parallel computing environment or …

Topic models have gained extensive consideration from the information retrieval research
community. As a result, the variety of extensions of topics modelling techniques have been …