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Table 2 Comparison of different algorithms on TWCV convergence with two data sets. Four algorithms, IGKA, FGKA, K-means and SOM are experimented on the two data set, the fig2data, and chodata. The TWCVs of IGKA and FGKA algorithm are obtained by averaging 10 individual runs while the generation number is set to 100, the population number is set to 50, the mutation probability is set to 0.005 for fig2data, and 0.0005 for chodata. The TWCV of K-means algorithm is obtained by averaging 20 individual runs. The TWCV of SOM is obtainedby 8 individual runs with different setting on X and Y dimension. The IGKA and FGKA algorithms have better TWCV convergence than the K-means and SOM.

From: Incremental genetic K-means algorithm and its application in gene expression data analysis

Algorithms Fig2data Chodata
IGKA (Average of 10 individual runs with generation 100, population 50, mutation probability 0.005 in fig2data, and 0.0005 in chodata) 4991.53889 16995.7
FGKA (Average of 10 individual runs with generation 100, population 50, mutation probability 0.005 in fig2data, and 0.0005 in chodata) 4992.13889 16995.4
K-means (Average of 20 individual runs) 5154.21434 17374.6758
SOM (Average of 8 individual runs with different setting) 24805.3661 21660.9049