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

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.