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A COMPARATIVE STUDY OF CLUSTERING METHODS

Publicat: 21 Aug 2006 | Vizualizari: 822

In this paper we propose a methodology for comparing clustering methods based on the quality of the result and the performance of the execution. We applied it to several known clustering methods: FastClust, Autoclass, Relational data analysis, and Kohonen nets. The quality of a clustering result depends on both the similarity measure used by the method and its implementation. An important feature of our methodology is a synthetic data generation program that allows producing data sets with specific (or desired) patterns using a combination of parameters, such as the number and the type of the attributes, the number of records, etc. We define a metric to measure the quality of a clustering method, i.e., its ability to discover some or all of the "hidden" patterns. The performance study is based on the resource consumption, i.e., CPU time and memory space.

 

Quiz

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