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However, traditional anomaly detection approaches often fail when dealing with high-dimensional data and limited system knowledge. To address this gap, this article aims to develop an effective unsupervised method for anomaly detection suitable for large-scale industrial contexts with minimal prior knowledge. The proposed Multi-block Local Outlier Factor (MLOF) method combines a variable decomposition technique based on Mutual Information and spectral clustering with a local anomaly detection algorithm using the Local Outlier Factor. The method was validated on the Tennessee Eastman Process and real-world industrial cases from Surface Mount Technology production lines, notably by comparing its results with 5 other methods in the literature. Results demonstrate a 15% improvement in anomaly detection performance compared to classical LOF on benchmark data and effective application in detecting anomalies in real production scenarios. 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