iVisClustering: An Interactive Visual Document Clustering via Topic Modeling

iVisClustering: An Interactive Visual Document Clustering via Topic Modeling

Clustering plays an important role in many large-scale data analyses providing users with an overall understanding of their data. Nonetheless, clustering is not an easy task due to noisy features and outliers existing in the data, and thus the clustering results obtained from automatic algorithms often do not make clear sense. To remedy this problem, automatic clustering should be complemented ...

Nonnegative matrix factorization for interactive topic modeling and document clustering

Nonnegative matrix factorization (NMF) approximates a nonnegative matrix by the product of two low-rank nonnegative matrices. Since it gives semantically meaningful result that is easily interpretable in clustering applications, NMF has been widely used as a clustering method especially for document data, and as a topic modeling method. We describe several fundamental facts of NMF and introduce...

Integrating Document Clustering and Topic Modeling

Document clustering and topic modeling are two closely related tasks which can mutually benefit each other. Topic modeling can project documents into a topic space which facilitates effective document clustering. Cluster labels discovered by document clustering can be incorporated into topic models to extract local topics specific to each cluster and global topics shared by all clusters. In thi...

Document Clustering Using Topic Analysis

Document Similarity Judgment for Interactive Document Clustering

This paper investigates the task of document similarity judgment for interactive document clustering. We suppose one of the promising approaches for developing next generation of web search engines is to incorporate user feedback mechanism into constrained clustering. As a basis for designing such search engines, it is important to study the interface design that can reduce user' burden of givi...