FLACON: An Information-Theoretic Approach to Flag-Aware Contextual Clustering for Large-Scale Document Organization

Organizing vast, heterogeneous enterprise documents is a critical challenge, as tradi-tional methods fail to capture the dynamic, multi-dimensional context (e.g., priority, workflow) that defines a document's true utility. This paper introduces FLACON (Flag-Aware Context-sensitive Clustering), a novel system that addresses this gap. FLACON models documents using a six-dimensional flag system—unifying semantic, temporal, priority, workflow, and relational contexts—and organizes them within an information-theoretic framework. The core objective is to minimize clustering entropy while maximizing the preservation of contextual information. The approach addresses gaps where context-aware systems lack domain-specific intelligence and LLM methods require prohibitive computational resources. FLACON provides deterministic, cost-effective organization with 7-fold performance improvement over LLM ap-proaches while achieving 89% of their clustering quality. Evaluation on nine dataset variations demonstrates significant improvements with Silhouette Scores of 0.311 versus 0.040 for traditional methods, representing 7.8-fold gains. The system demon-strates O(n log n) scalability and deterministic behavior suitable for compliance re-quirements.