AI RESEARCH
Near-optimal Linear Predictive Clustering in Non-separable Spaces via MIP and QPBO Reductions
arXiv CS.LG
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ArXi:2511.10809v3 Announce Type: replace Linear Predictive Clustering (LPC) partitions samples based on shared linear relationships between feature and target variables, with numerous applications including marketing, medicine, and education. Greedy optimization methods, commonly used for LPC, alternate between clustering and linear regression but lack global optimality. While effective for separable clusters, they struggle in non-separable settings where clusters overlap in feature space.