Automated taxonomy generation for summarizing multi-type relational datasets
Tao Li, Sarabjot Singh Anand · Warwick Research Archive Portal (University of Warwick) · 2008
Abstract — Taxonomy construction provides an efficient navigating and browsing mechanism to people by organizing large amounts of information into a small number of hierarchical clusters. Compared with manually editing taxonomies, Automated Taxonomy Generation has numerous advantages and has therefore been applied to categorize document collections. However, the utility of this technique to organize and represent relational datasets has not been investigated, because of its unaffordable computational complexity. In this paper we propose a new ATG method based on the relational clustering framework DIVA. By incorporating the idea of Representative Objects, the computational complexity can be greatly reduced. Moreover, we analyze the divergence of the data attributes and label the taxonomic nodes accordingly. The quality of the derived taxonomy is quantitatively evaluated by a synthesized criterion that considers both the intra-node homogeneity and inter-node heterogeneity. Theoretical analysis and experimental results prove that our approach is comparably effective and more efficient than other ATG algorithms. I.