MNR-FP-Growth: An Intelligent Pattern Mining Algorithm for Energy-Aware Redundancy Reduction in Building Management

Abstract

Effective building energy management (BEM) relies on extracting actionable intelligence from vast volumes of operational data. Association Rule Mining (ARM) is a cornerstone of knowledge discovery; however, traditional techniques face a dual challenge: either they generate a prohibitive volume of redundant patterns that obscure decision-making, or they employ aggressive pruning that compromises interpretability. In addition, existing approaches often treat energy awareness as a disjointed post-processing step rather than an intrinsic systemic constraint. To address these issues, this paper proposes Minimal Non-Redundant FP-Growth (MNR-FP-Growth), an intelligent algorithm that integrates energy-semantic redundancy pruning directly into the frequent pattern mining process. The core innovation lies in a multi-criteria pruning mechanism: a pattern is eliminated only when it is found to be both statistically redundant and operationally inefficient–defined as having equivalent support to a subset but an equal or higher energy penalty. This approach ensures the retention of a compact, non-redundant set of patterns with high operational utility. Through rigorous evaluation using a year of real-world building operational data, MNR-FP-Growth achieves an 11.2% reduction in pattern redundancy compared to standard FP-Growth. Moreover, the algorithm operates 61% faster than the state-of-the-art FP-Close while maintaining high structural integrity (Jaccard similarity $\ge$ 0.96). Applying the framework to rule generation, the analysis shows that 34% of derived rules provide zero-cost energy-saving opportunities, such as optimizing comfort parameters during low-occupancy intervals. By unifying statistical rigor with energy semantics, MNR-FP-Growth offers a robust, computationally efficient foundation for intelligent data-driven optimization in smart building systems.