The application of clustering methods for automatic taxonomy construction from text requires knowledge about the tradeoff between, (i), their effectiveness (quality of result), (ii), efficiency (run-time behaviour), and, (iii), traceability of the taxonomy construction by the ontology engineer. In this line, we present an original conceptual clustering method based on Formal Concept Analysis fo...

The application of clustering methods for automatic taxonomy construction from text requires knowledge about the tradeoff between, (i), their effectiveness (quality of result), (ii), efficiency (run-time behaviour), and, (iii), traceability of the taxonomy construction by the ontology engineer. In this line, we present an original conceptual clustering method based on Formal Concept Analysis fo...

We present a technique and a program for the automatic clustering of graphs. The technique is based on several heuristics, which allows for an eecient implementation on a personal computer. Our approach is capable of clustering graphs with > 3000 vertices eeciently. The demonstration shows an interactive user environment that supports both automatic and user-controlled clustering. As an applica...

Experimental evaluation of clustering techniques for component recovery is necessary in order to analyze their strengths and weaknesses in comparison to other techniques. For comparable evaluations of automatic clustering techniques, a common reference corpus of freely available systems is needed for which the actual components are known. The reference corpus is used to measure recall and preci...