Scalable Recursive Top-Down Hierarchical Clustering Approach with Implicit Model Selection for Textual Data Sets

Automatic generation of taxonomies can be useful for a wide area of applications. In our application scenario a topical hierarchy should be constructed reasonably fast from a large document collection to aid browsing of the data set. The hierarchy should also be used by the InfoSky projection algorithm to create an information landscape visualization suitable for explorative navigation of the data. We developed an algorithm that applies a scalable, recursive, top-down clustering approach to generate a dynamic concept hierarchy. The algorithm recursively applies a workflow consisting of preprocessing, clustering, cluster labeling and projection into 2D space. Besides presenting and discussing the benefits of combining hierarchy browsing with visual exploration, we also investigate the clustering results achieved on a real world data set. Keywords-topic hierarchy; landscape; growing k-means; model selection; vector space model;

• Type: Conference
• Year: 2010
• Where: DEXAW
• Authors: Markus Muhr, Vedran Sabol, Michael Granitzer
• Keywords: Data Set, Database, DEXAW 2010, Dynamic Concept Hierarchy, Topical Hierarchy