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AFGBStream: Adaptive Fuzzy Granular-Ball Framework for Stream Clustering With Sliding Windows
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DOI：10.1109/tfuzz.2026.3695688.png)
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Abstract
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Existing sliding window-based data stream clustering methods often struggle with fixed-granularity representations and delayed responses to abrupt pattern changes, leading to reduced adaptability and clustering accuracy. To address these limitations, we propose an <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A</u>daptive <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">F</u>uzzy <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">G</u>ranular-<underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">B</u>all framework for <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Stream</u> clustering with sliding windows (AFGBStream), a novel sliding window-based data stream clustering framework that integrates fuzzy set theory with granular-ball computing to support adaptive and incremental updates. Specifically, a window-guided data granulation strategy is developed using a fuzzy C-means (FCM)-based granular-ball generation approach, which enhances representation robustness and reduces sensitivity to microcluster parameters. In addition, a window-aware temporal decay mechanism is employed to improve the model’s responsiveness to evolving data by dynamically filtering outdated information. Our comprehensive experimental evaluation on 17 benchmark datasets demonstrates that AFGBStream achieves superior performance over five competing methods.
Keywords:
Clustering
data stream
granular ball (GB)
granular computing
sliding window
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