Initializing partition-optimization algorithms.
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.
Maitra
Ranjan
R
Department of Statistics, Iowa State University, Ames, IA 50011-1210, USA. maitra@iastate.edu
D000465:Algorithms; D017360:Arabidopsis / Q000235:genetics / Q000378:metabolism; D055884:Chemical Hazard Release / Q000706:statistics & numerical data; D002940:Circadian Rhythm / Q000235:genetics; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods*; D003627:Data Interpretation, Statistical; D029968:Escherichia coli Proteins / Q000235:genetics; D006801:Humans; D007220:Industrial Waste / Q000706:statistics & numerical data; D008767:Methylmercury Compounds; D016011:Normal Distribution; D020411:Oligonucleotide Array Sequence Analysis; D010363:Pattern Recognition, Automated / Q000379:methods*; D013213:Starch / Q000096:biosynthesis / Q000235:genetics
D029968:Escherichia coli Proteins; D007220:Industrial Waste; D008767:Methylmercury Compounds; D013213:Starch