Topic Models and Fusion Methods: a Union to Improve Text Clustering and Cluster Labeling

TitleTopic Models and Fusion Methods: a Union to Improve Text Clustering and Cluster Labeling
Publication TypeJournal Article
Year of Publication2019
AuthorsPourvali, M., S. Orlando, and H. Omidvarborna
JournalInternational Journal of Interactive Multimedia and Artificial Intelligence
ISSN1989-1660
IssueSpecial Issue on Artificial Intelligence Applications
Volume5
Number4
Date Published03/2019
Pagination28-34
Abstract

Topic modeling algorithms are statistical methods that aim to discover the topics running through the text documents. Using topic models in machine learning and text mining is popular due to its applicability in inferring the latent topic structure of a corpus. In this paper, we represent an enriching document approach, using state-of-the-art topic models and data fusion methods, to enrich documents of a collection with the aim of improving the quality of text clustering and cluster labeling. We propose a bi-vector space model in which every document of the corpus is represented by two vectors: one is generated based on the fusion-based topic modeling approach, and one simply is the traditional vector model. Our experiments on various datasets show that using a combination of topic modeling and fusion methods to create documents’ vectors can significantly improve the quality of the results in clustering the documents.

KeywordsCluster Labeling, Document Clustering, Document Enriching, Text Mining
DOI10.9781/ijimai.2018.12.007
URLhttp://www.ijimai.org/journal/sites/default/files/files/2018/12/ijimai_5_4_3_pdf_49140.pdf
AttachmentSize
ijimai_5_4_3.pdf2.04 MB