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Module 4: Clustering, K-means, K-medoids, Density-based Hierarchical, Spectral .

20232m

(b) A Machine Learning technique that helps in detecting the outliers in data is called as:

  • (i) Clustering .
    (ii) Classification
    (iii) Anomaly Detection
    (iv) All of the above
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Answer: (iii) Anomaly Detection

Explanation:

  • Anomaly Detection (also called outlier detection) is the ML technique explicitly designed to identify rare items, events or observations which raise suspicions by differing significantly from the majority of the data.
  • Clustering groups similar data points; outliers may be found as points not belonging to any cluster, but it is not its primary purpose.
  • Classification assigns data to predefined classes; it needs labeled outlier examples which are rarely available.
  • Techniques used for Anomaly Detection: Statistical (Z-score, Gaussian), Density-based (LOF, DBSCAN), Distance-based, Isolation Forest, One-Class SVM, Autoencoders.

Hence the most specific answer is (iii) Anomaly Detection. Option (iv) All of the above is not best choice because clustering/classification are not primarily outlier detectors.

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