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Figure 2 | BMC Bioinformatics

Figure 2

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

Figure 2

The impacts of mutation probability on time performance for IGKA and FGKA. The population size is set to 50; the generation size is set to 100. The mutation probability ranges from 0.001 to 0.1 for fig2data, and 0.0001 to 0.1 for chodata. (A) shows the running time for FGKA and IGKA on fig2data. (B) shows the running time for FGKA and IGKA on chodata. (C) shows the average and standard error of running time on fig2data when the mutation probability is set to 0.001 and 0.005. (D) shows the average and standard error of running time on chodata when the mutation probability is set to 0.0001 and 0.0005. When the mutation probability increases, the running time increases accordingly for both algorithms. However, when the mutation probability is smaller than some threshold (0.005 for fig2data, and 0.0005 for chodata), the IGKA has better performance. It indicates the thresholds vary from one dataset to another. It mainly depends on the number of patterns and the number of features in the data set.

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