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Initializing Partition-Optimization Algorithms

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1 Author(s)
Maitra, R. ; Dept. of Stat., Iowa State Univ., Ames, IA

Clustering datasets is a challenging problem needed in a wide array of applications. Partition-optimization approaches, such as k-means or expectation-maximization (EM) algorithms, are sub-optimal and find solutions in the vicinity of their initialization. This paper proposes a staged approach to specifying initial values by finding a large number of local modes and then obtaining representatives from the most separated ones. Results on test experiments are excellent. We also provide a detailed comparative assessment of the suggested algorithm with many commonly-used initialization approaches in the literature. Finally, the methodology is applied to two datasets on diurnal microarray gene expressions and industrial releases of mercury.

Published in:

Computational Biology and Bioinformatics, IEEE/ACM Transactions on  (Volume:6 ,  Issue: 1 )

Date of Publication:

Jan.-March 2009

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