  This paper introduces a simple and efficient density estimator that enables
fast systematic search. To show its advantage over commonly used kernel density
estimator, we apply it to outlying aspects mining. Outlying aspects mining
discovers feature subsets (or subspaces) that describe how a query stand out
from a given dataset. The task demands a systematic search of subspaces. We
identify that existing outlying aspects miners are restricted to datasets with
small data size and dimensions because they employ kernel density estimator,
which is computationally expensive, for subspace assessments. We show that a
recent outlying aspects miner can run orders of magnitude faster by simply
replacing its density estimator with the proposed density estimator, enabling
it to deal with large datasets with thousands of dimensions that would
otherwise be impossible.
