The core of a k-means algorithm is the reallocation phase. A varietyof schemes have been suggested for moving entities from one cluster to another and each of them maygive a di(erent clustering even though the data set is the same. The present paper describes shortcomings and relative merits of 17 relocation methods in connection with randomlygenerated data sets.

A computational study of several relocation methods for k-means algorithms

TARSITANO, Agostino
2003

Abstract

The core of a k-means algorithm is the reallocation phase. A varietyof schemes have been suggested for moving entities from one cluster to another and each of them maygive a di(erent clustering even though the data set is the same. The present paper describes shortcomings and relative merits of 17 relocation methods in connection with randomlygenerated data sets.
Combinatorial optimization; Iterative partitioning; Non-hierarchical classification
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/20.500.11770/137425
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