The original Cardiotocography (Cardio) dataset from UCI machine learning repository consists of measurements of fetal heart rate (FHR) and uterine contraction (UC) features on cardiotocograms classified by expert obstetricians. This is a classification dataset, where the classes are normal, suspect, and pathologic. For outlier detection, The normal class formed the inliers, while the pathologic (outlier) class is downsampled to 176 points. The suspect class is discarded.
C. C. Aggarwal and S. Sathe, “Theoretical foundations and algorithms for outlier ensembles.” ACM SIGKDD Explorations Newsletter, vol. 17, no. 1, pp. 24–47, 2015.
Saket Sathe and Charu C. Aggarwal. LODES: Local Density meets Spectral Outlier Detection. SIAM Conference on Data Mining, 2016.
Description: X = Multi-dimensional point data, y = labels (1 = outliers, 0 = inliers)