The original arrhythmia dataset from UCI machine learning repository is a multi-class classification dataset with dimensionality 279. There are five categorical attributes which are discarded here, totalling 274 attributes. The smallest classes, i.e., 3, 4, 5, 7, 8, 9, 14, 15 are combined to form the outliers class and the rest of the classes are combined to form the inliers class.
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K. M. Ting, J. T. S. Chuan, and F. T. Liu. “Mass: A New Ranking Measure for Anomaly Detection.“, IEEE Transactions on Knowledge and Data Engineering, 2009.
F. Keller, E. Muller, K. Bohm.“HiCS: High-contrast subspaces for density-based outlier ranking.” ICDE, 2012.
Description: X = Multi-dimensional point data, y = labels (1 = outliers, 0 = inliers)