Abstract

Clustering has many uses such as the generation of taxonomies and concept formation. It is essentially a search through a model space to maximise a given criterion. The criterion aims to guide the search to find models that are suitable for a purpose. The search's aim is to efficiently and consistently find the model that gives the optimal criterion value. Considerable research has occurred into the criteria to use but minimal research has studied how to best search the model space. We describe how we have used simulated annealing to search the model space to optimise the minimum message length criterion.

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