01590nas a2200229   4500000000100000000000100001008004100002260001200043653001600055653002300071653002400094653002100118100002000139700002200159700002300181245009300204856009500297300001000392490000600402520093800408022001401346       2019                            d  c03/201910aText Mining10aDocument Enriching10aDocument Clustering10aCluster Labeling1 aMohsen Pourvali1 aSalvatore Orlando1 aHosna Omidvarborna00aTopic Models and Fusion Methods: a Union to Improve Text Clustering and Cluster Labeling  uhttp://www.ijimai.org/journal/sites/default/files/files/2018/12/ijimai_5_4_3_pdf_49140.pdf  a28-340 v53 aTopic 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.  a1989-1660