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Table 10

Comparison between the best SOMFCM clustering scenarios based on conventional fuzzy validation indices and proposed techniques

The best clustering scenarioClustering validation typeOptimum mOptimum cVEXBVKVFSMARD (%)CImean(Umax)meanN_UmaxR2
Four-component (TPI-CN-Distance-P50) Conventional index 1.7 8 0.516 3,225 −8,738 3.25 0.535 0.615 938 0.632 
Four-component (TPI-CN-Distance-P50) Proposed technique 1.6 6 0.618 3,851 −10,109 2.81 0.477 0.684 1,976 0.593 
Three-component (TPI-CN-P50) Conventional index 1.7 7 0.329 2,266 −9,620 15.31 0.38 0.739 2,266 0.716 
Three-component (TPI-CN-P50) Proposed technique 1.6 6 0.386 2,416 −10,509 3.13 0.326 0.788 3,261 0.739 
1.7 6 0.376 2,355 −9,533 3.51 0.385 0.731 2,686 0.737 
Three-component (TPI-Distance-P50) Proposed technique 1.6 6 0.647 4,184 −9,975 3.53 0.405 0.738 2,659 0.657 
The best clustering scenarioClustering validation typeOptimum mOptimum cVEXBVKVFSMARD (%)CImean(Umax)meanN_UmaxR2
Four-component (TPI-CN-Distance-P50) Conventional index 1.7 8 0.516 3,225 −8,738 3.25 0.535 0.615 938 0.632 
Four-component (TPI-CN-Distance-P50) Proposed technique 1.6 6 0.618 3,851 −10,109 2.81 0.477 0.684 1,976 0.593 
Three-component (TPI-CN-P50) Conventional index 1.7 7 0.329 2,266 −9,620 15.31 0.38 0.739 2,266 0.716 
Three-component (TPI-CN-P50) Proposed technique 1.6 6 0.386 2,416 −10,509 3.13 0.326 0.788 3,261 0.739 
1.7 6 0.376 2,355 −9,533 3.51 0.385 0.731 2,686 0.737 
Three-component (TPI-Distance-P50) Proposed technique 1.6 6 0.647 4,184 −9,975 3.53 0.405 0.738 2,659 0.657 
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