The original Wine dataset from UCI machine learning repository is a multiclass classification dataset having 13 attributes and 3 classes. These data are the results of a chemical analysis of wines grown in the same region in Italy but derived from three different cultivars. The analysis determined the quantities of 13 constituents found in each of the three types of wines. Class 2 and 3 are used as inliers and class 1 is downsampled to 10 instances to be used as ouliers.
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)