Analysis of structural relationships for hierarchical cluster labeling

Cluster label quality is crucial for browsing topic hierarchies obtained via document clustering. Intuitively, the hierarchical structure should influence the labeling accuracy. However, most labeling algorithms ignore such structural properties and therefore, the impact of hierarchical structures on the labeling accuracy is yet unclear. In our work we integrate hierarchical information, i.e. sibling and parentchild relations, in the cluster labeling process. We adapt standard labeling approaches, namely Maximum Term Frequency, Jensen-Shannon Divergence, 2 Test, and Information Gain, to take use of those relationships and evaluate their impact on 4 different datasets, namely the Open Directory Project, Wikipedia, TREC Ohsumed and the CLEF IP European Patent dataset. We show, that hierarchical relationships can be exploited to increase labeling accuracy especially on high-level nodes. Categories and Subject Descriptors H.3.1 [Content Analysis and Indexing]: Linguistic processing; H.3.3...

• Type: Conference
• Year: 2010
• Where: SIGIR
• Authors: Markus Muhr, Roman Kern, Michael Granitzer
• Keywords: Hierarchical Structures, Information Technology, SIGIR 2010, Standard Labeling Approaches, Topic Hierarchies