The original Seismic dataset from UCI machine learning repository is a binary classification dataset having 19 attributes. It is an unbalanced dataset where the positive (hazard) class is a minority class and considered as outlier class and the negative class (no hazard) is considered as inlier class. Out of 19 attributes 11 are utilized for outlier detection.
Saket Sathe and Charu C. Aggarwal. LODES: Local Density meets Spectral Outlier Detection. SIAM Conference on Data Mining, 2016.
Description: labels (1 = outliers, 0 = inliers)