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Abstract

Various ensemble methods are proposed to aggregate hierarchical clusterings. These methods combine a set of hierarchical clusterings into a single representative clustering with an improved quality. The quality of this representative hierarchical clustering intensively depends on the aggregation operator (aggregator) used in the combination. However, choosing from the large pool of aggregators is a challenging task. To facilitate this task, in this paper, we first introduce aggregator types, triangular norms and averaging operators, and then compile a list of main properties and parametric characteristics of these aggregators. An extra property which is needed in hierarchical clustering combination is also defined. Thereafter, a set of experiments is designed to elect the optimized hierarchy aggregator from the variety of these aggregators. The out coming results from the experiments are proved to be compatible with the previous applications in the field of hierarchical clustering ensembles.

Keywords

Aggregation operator ensemble learning hierarchical clustering triangular norm averaging operator

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Optimized aggregation function in hierarchical clustering combination

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