A data-science team must deploy a real-time anomaly-detection system for a fleet of IoT-enabled manufacturing devices. The system has to spot previously unseen equipment-failure patterns in high-dimensional, unlabeled telemetry data while keeping computational overhead low. Which approach is most suitable for this task?
The correct answer is Isolation Forest. The algorithm builds an ensemble of random trees to 'isolate' rare points; anomalies generally need fewer partitions, so they receive high anomaly scores. Isolation Forest has roughly linear complexity with respect to the number of samples and uses little memory, making it practical for high-dimensional, high-volume streams.
Local Outlier Factor (LOF) and k-Nearest Neighbors (k-NN) are distance- or density-based. Their pairwise-distance calculations yield quadratic-scale costs, and their effectiveness drops sharply in high dimensions, so they are ill-suited to real-time processing.
An Autoencoder requires a separate training phase on predominantly normal data, and deep-network training is computationally intensive. Moreover, anomalies are not guaranteed to produce large reconstruction errors, so genuinely novel faults can be missed.
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Why is the Isolation Forest suitable for high-dimensional, unlabeled data?
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Why are methods like LOF and k-NN less effective in high dimensions?
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Why is an Autoencoder not the best choice for this scenario?