  We propose a new method, named isolation Mondrian forest (iMondrian forest),
for batch and online anomaly detection. The proposed method is a novel hybrid
of isolation forest and Mondrian forest which are existing methods for batch
anomaly detection and online random forest, respectively. iMondrian forest
takes the idea of isolation, using the depth of a node in a tree, and
implements it in the Mondrian forest structure. The result is a new data
structure which can accept streaming data in an online manner while being used
for anomaly detection. Our experiments show that iMondrian forest mostly
performs better than isolation forest in batch settings and has better or
comparable performance against other batch and online anomaly detection
methods.
