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Life sciences · Preprint
arXiv · September 6, 2026
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Sector-Mean is a deterministic initialization strategy for K-Means clustering that reduces initialization time by 74.9% versus K-Means++ and 59.8% versus Max-Min, while maintaining equivalent clustering quality and requiring approximately 5–16% fewer Lloyd iterations. The method is evaluated on two-dimensional benchmarks and real-world datasets using Friedman and Nemenyi post-hoc statistical tests.
Comparative algorithmic evaluation on benchmarks and real-world datasets. Two-dimensional benchmark datasets (SIPU, Birch) and multiple real-world datasets; specific datasets and their characteristics not detailed in abstract. Intervention: Sector-Mean Initialization: deterministic centroid initialization via angular sector partitioning with O(N) time complexity. Compared with: Random initialization, K-Means++, and Max-Min initialization.
Sector-Mean reduces initialization time by 74.9% compared to K-Means++ and 59.8% compared to Max-Min Sector-Mean achieves approximately 5% fewer iterations than K-Means++ and 16% fewer than Max-Min Clustering quality equivalent to K-Means++ and Max-Min (Friedman test p<0.05, Nemenyi post-hoc comparison)
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A computational method paper presenting a novel deterministic initialization algorithm with experimental validation on benchmarks and real-world datasets, but lacking peer review and addressing an algorithmic optimization rather than a clinical or biological outcome.
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K-Means is one of the most widely used clustering algorithms, but its susceptibility to initial centroid selection remains a primary bottleneck for its convergence speed and clustering accuracy. This paper proposes Sector-Mean Initialization, a deterministic initialization strategy with O(N) time complexity that partitions the two-dimensional data space into angular sectors around the global centroid and initializes centroids using sector-wise means. We evaluate the method on established two-dimensional benchmarks (SIPU, Birch) and multiple real-world datasets, comparing against random, K-Means++, and Max-Min initialization under identical Lloyd iterations. The statistical analysis of Friedman's test (p<0.05) and Nemenyi post-hoc comparison indicates that, while delivering equivalent clustering quality as K-Means++ and Max-Min, Sector-Mean offers significant computational efficiency. Experimental results show that Sector-Mean reduces the initialization time by 74.9% and 59.8% in comparison to K-Means++ and max-min, respectively. And, it yields the lowest average number of iterations, achieving approximately 5% fewer iterations than K-Means++ and 16% fewer than max-min. These results highlight that Sector-Mean initialization offers a deterministic and computationally efficient initialization strategy while preserving cluster quality.
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