Evaluation of multiple clustering solutions

Hans Peter Kriegel, Erich Schubert, Arthur Zimek

Research output: Contribution to journalConference articleResearchpeer-review


Though numerous new clustering algorithms are proposed every year, the fundamental question of the proper way to evaluate new clustering algorithms has not been satisfactorily answered. Common procedures of evaluating a clustering result have several drawbacks. Here, we propose a system that could represent a step forward in addressing open issues (though not resolving all open issues) by bridging the gap between an automatic evaluation using mathematical models or known class labels and the actual human researcher. We introduce an interactive evaluation method where clusters are first rated by the system with respect to their similarity to known results and where "new" results are fed back to the human researcher for inspection. The researcher can then validate and refine these results and re-add them back into the system to improve the evaluation result.

Original languageEnglish
JournalCEUR Workshop Proceedings
Pages (from-to)55-66
Publication statusPublished - 2011
Externally publishedYes
Event2nd Workshop on Discovering, Summarizing and Using Multiple Clusterings - Athens, Greece
Duration: 5. Sep 20115. Sep 2011


Conference2nd Workshop on Discovering, Summarizing and Using Multiple Clusterings


Dive into the research topics of 'Evaluation of multiple clustering solutions'. Together they form a unique fingerprint.

Cite this