I'm currently working on a project that requires the use of unsupervised anomaly detection, but I'm unable to find a relavent data set, so I'm considering the following option:
Assuming I have a data set X of m examples labeled using K classes. Let X(k) be the subset of X where all examples are labeled as k, and k_max be the larget class. Can I use X(k_max) as a training set for an anomaly detector, whose task is to flag elements who weren't labeled as k_max, as anomaly? Using p << [m - size(X(k))] of the remaining examples in X for cv and test sets as the anomalous examples.