In traditional text clustering, documents appear terms frequency without considering the semantic information of each document (i.e., vector model). The property of vector model may be incorrectly classified documents into different clusters when documents of same cluster lack the shared terms. Recently, to overcome this problem uses knowledge based approaches. However, these approaches have an...

Analyzing work functions and the IO variables they need is an important component of designing and evaluating complex systems. We develop a biclustering method for jointly grouping work functions and IO variables. Given a binary matrix indicating which IO variables are required for which work functions, we develop a computational method for finding dense groups of related tasks and information....

We present a visual analytics system for large-scale document retrieval tasks with high recall where any missing relevant documents can be critical. Our system utilizes a novel user-driven topic modeling called targeted topic modeling, a variant of nonnegative matrix factorization (NMF). Our system visualizes a topic summary in a treemap form and lets users keep relevant topics and incrementall...

Given a collection of data points, non-negative matrix factorization (NMF) suggests to express them as convex combinations of a small set of ‘archetypes’ with non-negative entries. This decomposition is unique only if the true archetypes are non-negative and sufficiently sparse (or the weights are sufficiently sparse), a regime that is captured by the separability condition and its generalizati...

Ranking algorithms have been widely used for web and other networks to infer quality/popularity. Both PageRank and HITS were developed for ranking web pages from a web reference graph. Nevertheless, these algorithms have also been applied extensively for a variety of other applications such as question-answer services, author-paper graphs, and others where a graph can be deduced from the data s...

Projective non-negative matrix factorization (PNMF) projects high-dimensional non-negative examples X onto a lower-dimensional subspace spanned by a non-negative basis W and considers W(T) X as their coefficients, i.e., X≈WW(T) X. Since PNMF learns the natural parts-based representation Wof X, it has been widely used in many fields such as pattern recognition and computer vision. However, PNMF ...

Topic models have been extensively used to organize and interpret the contents of large, unstructured corpora of text documents. Although topic models often perform well on traditional training vs. test set evaluations, it is often the case that the results of a topic model do not align with human interpretation. This interpretability fallacy is largely due to the unsupervised nature of topic m...

Non-negative matrix factorization (NMF), proposed recently by Lee and Seung, has been applied to many areas such as dimensionality reduction, image classification image compression, and so on. Based on traditional NMF, researchers have put forward several new algorithms to improve its performance. However, particular emphasis has to be placed on the initialization of NMF because of its local co...