Probabilistic Granular Neural Clustering Handles Imbalanced Machine Sensor Streams with Adaptive Granules Online Streaming Data
Keywords:
stream clustering, granular computing, imbalanced data, neural networks, sensor networksAbstract
The rapid proliferation of interconnected sensors in contemporary industrial environments has led to the generation of massive, high-velocity data streams. A critical challenge in analyzing these continuous data flows is the inherent imbalance and volatility of the underlying classes, particularly in machine health monitoring and predictive maintenance where anomalous events are exceedingly rare compared to normal operational states. Traditional clustering algorithms often fail to adequately capture minority cluster structures, leading to the misidentification or complete neglect of crucial anomaly signatures. To address this persistent issue, this paper proposes a novel framework designated as Probabilistic Granular Neural Clustering. The proposed methodology leverages granular computing principles to encapsulate local data geometries into information granules, which are subsequently processed by a probabilistic neural architecture designed to model the uncertainty and dynamic drift of data streams. By incorporating a probabilistic assignment mechanism, the framework dynamically adjusts cluster boundaries and mitigates the overshadowing effects of majority classes. Extensive experimental evaluations are conducted across multiple imbalanced machine sensor datasets. The analytical outcomes demonstrate that the proposed granular neural approach significantly enhances the detection and cohesive grouping of minority instances while maintaining the structural integrity of majority clusters. The framework offers a robust, scalable solution for unsupervised learning in non-stationary and heavily skewed industrial data environments.
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Copyright (c) 2026 Matteo Lombardi, Etienne Rey, Nathan Henderson, Logan Armstrong
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