A New Methodology to Compare Clustering Algorithms

Authors:
Céline Robardet;Fabien Feschet
Affiliations:
-;-
Venue:
IDEAL '00 Proceedings of the Second International Conference on Intelligent Data Engineering and Automated Learning, Data Mining, Financial Engineering, and Intelligent Agents
Year:
2000

Citing 4
Cited 0

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Abstract

In the context of unsupervised clustering, lots of different algorithms have been proposed. Most of them consist in optimizing an objective function using a search strategy. We present here a new methodology for studying and comparing the performances of the objective functions and search strategies employed.