Abstract:
The Kohonen Self-Organizing Map (SOM) is an unsupervised learning technique for summarizing high-dimensional data so that similar inputs are, in general, mapped close to one another. When applied to textual data, SOM has been shown to be able to group together related concepts in a data collection and to present major topics within the collection with larger regions. This article presents research in which we sought to validate these properties of SOM, called the Proximity and Size Hypotheses, through a user evaluation study. Building upon our previous research in automatic concept generation and classification, we demonstrated that the Kohonen SOM was able to perform concept clustering effectively, based on its concept precision and recall7 scores as judged by human experts. We also demonstrated a positive relationship between the size of an SOM region and the number of documents contained in the region. We believe this research has established the Kohonen SOM algorithm as an intuitively appealing and promising neural-network-based textual classification technique for addressing part of the longstanding "information overload" problem.
Abstract:
This paper describes a study to explore the integration of Group Support Systems (GSS) and Artificial Intelligence (AI) technology to provide solicitation, analytical, visualization and sense-making support for attitudes from large distributed marketing focus groups. The paper describes two experiments and the concomitant evolutionary design and development of an attitude analysis process and the MindMine Comment Analysis Tool. The analysis process circumvents many of the problems associated with traditional data gathering via closed-ended questionnaires and potentially biased interviews by providing support for online free response evaluative comments. MindMine allows teams of raters to analyze comments from any source, including electronic meetings, discussion groups or surveys, whether they are Web-based or same-place. The analysis results are then displayed as visualizations that enable the team quickly to make sense of attitudes reflected in the comment set, which we believe provide richer information and a more detailed understanding of attitudes.
Abstract:
The Illinois Digital Library Project has developed an infrastructure for federated repositories. The deployed testbed indexes articles from many scientific journals and publishers in a production stream that can be searched as though they form a single collection.
Abstract:
Analysis of crime data has long been a labor-intensive effort. Crime analysts are required to query numerous databases and sort through results manually. To alleviate this, we have integrated three different visualization techniques into one application called the Spatio Temporal Visualizer (STV). STV includes three views: a timeline; a periodic display; and a Geographic Information System (GIS). This allows for the dynamic exploration of criminal data and provides a visualization tool for our ongoing COPLINK project. This paper describes STV, its various components, and some of the lessons learned through interviews with target users at the Tucson Police Department. © Springer-Verlag Berlin Heidelberg 2003.
