Clustering of 10 datasets (generated after performing 10 attribute weighting algorithms) into T (mesophile) and F (thermophile) classes by four different unsupervised clustering algorithms (K-Means, K-Medoids, SVC and EMC).

The actual numbers of T (mesostable) and F (thermostable) classes in the original datasets were 1544 and 513, respectively. The highest accuracy (100%) was observed when the EMC clustering method was applied to datasets generated by Correlation and Uncertainty attribute weighting algorithms that highlighted in the table.