Two-phase clustering process for outliers detection

By M. Jiang, S. Tseng, Chih-Ming Su. Pattern Recognition Letters, 2001

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Outlier Analysis (2013, background): “A number of outlier detection methods have also been proposed for cases where the anomalies may lie in small clusters [155, 371, 372, 351, 208, 239].”
DataComp: In search of the next generation of multimodal datasets (2023, background): “[66] Mon-Fong Jiang, Shian-Shyong Tseng, and Chih-Ming Su.”
Progress in Outlier Detection Techniques: A Survey (2019, background): “In some cases [42], [43], visual examination of the data is more preferred to get a clear picture of the degree of”
Anomaly detection: A survey (2009, background): “Several variations of the third category of techniques have been proposed [Pires and Santos-Pereira 2005; Otey et al. 2003; Eskin et al. 2002; Mahoney et al. 2003; Jiang et al. 2001; He et al. 2003].”
Discovering cluster-based local outliers (2003, methodology): “In this case, the method proposed by Jiang et al. (2001) failed to fulfill this task effectively.”
Deep Learning for Anomaly Detection (2020, methodology): “After the deep clustering, the cluster assignments in the resulting f function can then be utilized to compute anomaly scores based on [60, 67, 68, 142].”
Scalable K-Means++ (2012, background): “Several papers have studied k -means with outliers; see, for example, [22] and the references in [18].”