With the rapid growth of digital publishing, harvesting, managing, and analyzing scholarly
information have become increasingly challenging. The term Big Scholarly Data is coined …

Clustering is a widely studied data mining problem in the text domains. The problem finds
numerous applications in customer segmentation, classification, collaborative filtering …

Statistical topic models can help analysts discover patterns in large text corpora by
identifying recurring sets of words and enabling exploration by topical concepts. However …

In this paper, we present a novel exploratory visual analytic system called TIARA (Text
Insight via Automated Responsive Analytics), which combines text analytics and interactive …

Web search result clustering aims to facilitate information search on the Web. Rather than
the results of a query being presented as a flat list, they are grouped on the basis of their …

In this paper, we present a novel approach to Web search result clustering based on the
automatic discovery of word senses from raw text, a task referred to as Word Sense …

During the information search and retrieval (ISR) process, user-system interactions such as
submitting queries, examining results, and engaging with information impose some degree …

We present a scalable technique to explore surveillance videos by scatter/gather browsing
of trajectories of moving objects. Trajectories are clustered according to a variety of …

Document clustering offers the potential of supporting users in interactive retrieval,
especially when users have problems in specifying their information need precisely. In this …

Web search engines are important gateways for users to access health information. This
study explored whether a search interface based on the Bing API and enabled by …