Search this site
Embedded Files
Skip to main content
Skip to navigation
TSE2020-Decision Clustering
Home
Appendix
Systems
System Parameters
Study Steps
Tool Features
Pre-study Survey
Post-study Survey
Evaluation Results
TSE2020-Decision Clustering
Home
Appendix
Systems
System Parameters
Study Steps
Tool Features
Pre-study Survey
Post-study Survey
Evaluation Results
More
Home
Appendix
Systems
System Parameters
Study Steps
Tool Features
Pre-study Survey
Post-study Survey
Evaluation Results
System Parameters
Multi-objective Optimization
Algorithm:
NSGAII
Population size:
100
Max Evaluation*:
100,000
Selection Operator:
Binary
Cross Over Operator:
Single Point
Cross Over P:
0.7
Mutation Operator:
Bit Flip
Mutation P:
0.4
Gene Modification P:
0.5
Min Refactoring:
10
Max Refactoring:
30
Clusterings
Best # of Clusters Measure:
Calinski Harabaz (
The CH index is known to perform very well when the size of the data is not large such as our case.
)
Compared # of Clusters:
3-10
GMM # of Components:
Best # of Clusters
GMM Covariance Type:
Full
GMM Regularization:
0.000001
GMM Max # of Iterations:
1000
GMM Convergence Threshold:
0.0001
GMM Weight Initialization Method:
KMeans
Google Sites
Report abuse
Google Sites
Report abuse