Bi-Partitioned Feature-Weighted K -Means Clustering for Detecting Insurance Fraud Claim Patterns

Francis Kwaku Combert, Shengkun Xie () and Anna T. Lawniczak
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Francis Kwaku Combert: Mathematics and Statistics, University of Guelph, Guelph, ON N1G 2W1, Canada
Shengkun Xie: Global Management Studies, Ted Rogers School of Management, Toronto Metropolitan University, Toronto, ON M5B 2K3, Canada
Anna T. Lawniczak: Mathematics and Statistics, University of Guelph, Guelph, ON N1G 2W1, Canada

Mathematics, 2025, vol. 13, issue 3, 1-47

Abstract: The weighted K -means clustering algorithm is widely recognized for its ability to assign varying importance to features in clustering tasks. This paper introduces an enhanced version of the algorithm, incorporating a bi-partitioning strategy to segregate feature sets, thus improving its adaptability to high-dimensional and heterogeneous datasets. The proposed bi-partition weighted K -means (BPW K -means) clustering approach is tailored to address challenges in identifying patterns within datasets with distinct feature subspaces, such as those in insurance claim fraud detection. Experimental evaluations on real-world insurance datasets highlight significant improvements in both clustering accuracy and interpretability compared to the classical K -means, achieving an accuracy of approximately 91%, representing an improvement of about 38% over the classical K -means algorithm. Moreover, the method’s ability to uncover meaningful fraud-related clusters underscores its potential as a robust tool for fraud detection. Beyond insurance, the proposed framework applies to diverse domains where data heterogeneity demands refined clustering solutions. The application of the BPW K-means method to multiple real-world datasets highlights its clear superiority over the classical K-means algorithm.

Keywords: K-means clustering; machine Learning; feature selection; insurance fraud detection (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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