SOURCE-LINKED INTELLIGENCE

Sector-Mean: Deterministic Initialization of K-Means Centroids via Angular Sector Partitioning

arXiv · AI, language, vision and robotics · article · Sep 6, 2026 · UTC

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-Mean

Read original source ↗ Open in workspace

recordType

paper

region

Global