Relationally Guided History Transfer for Streaming Ensemble Clustering

Streaming ensemble clustering seeks to maintain a consensus from base partitions as objects enter and leave successive windows. Existing static ensemble clustering methods, when applied independently to each window, discard historical structure carried by persistent objects. However, directly preserving the preceding consensus can retain outdated structure and provides no historical guidance for newly arrived objects. We propose AGILE, a streaming ensemble clustering framework that uses current base partitions to extend history beyond the window overlap and determine how strongly it should influence the new consensus. Pairwise intersections of base partitions define sparse relations through which historical information reaches arriving objects. To account for redundancy and disagreement among base partitions, AGILE draws complementary evidence from partitions corroborated by their peers and encodes relational corrections with relative-support-weighted signed factors. The corrected current structure supports a history-free consensus against which historical compatibility is assessed. An agreement–coverage gate then regulates the propagated history according to its consistency with the current consensus and cluster representation on the overlap. We establish monotone objective convergence, stationarity of fixed points, and linear time complexity in the window size. Experiments under different window overlap rates demonstrate the effectiveness of AGILE. The source code is available in the supplementary material.