Session 9 : Recent Developments in the Application of Pairwise Overlap
Presentation Type
Invited
Student
No
Track
Methodology
Abstract
Pairwise overlap is defined as the sum of misclassification probabilities calculated for a pair of mixture components. It is useful for studying the systematic performance of clustering algorithms. Currently, existing methods allow simulating elliptically distributed data according to pre-specified overlap characteristics. An approach to simulating skewed clusters with a desired overlap is proposed. Next, an extension to measuring overlap in cluster-weighted models is discussed.
Start Date
2-7-2025 2:30 PM
End Date
2-7-2025 3:30 PM
Session 9 : Recent Developments in the Application of Pairwise Overlap
Dakota A & C (Room 250)
Pairwise overlap is defined as the sum of misclassification probabilities calculated for a pair of mixture components. It is useful for studying the systematic performance of clustering algorithms. Currently, existing methods allow simulating elliptically distributed data according to pre-specified overlap characteristics. An approach to simulating skewed clusters with a desired overlap is proposed. Next, an extension to measuring overlap in cluster-weighted models is discussed.