This book discusses a variety of methods for outlier ensembles and
organizes them by the specific principles with which accuracy
improvements are achieved. In addition, it covers the techniques with
which such methods can be made more effective. A formal classification
of these methods is provided, and the circumstances in which they work
well are examined. The authors cover how outlier ensembles relate (both
theoretically and practically) to the ensemble techniques used commonly
for other data mining problems like classification. The similarities and
(subtle) differences in the ensemble techniques for the classification
and outlier detection problems are explored. These subtle differences do
impact the design of ensemble algorithms for the latter problem. This
book can be used for courses in data mining and related curricula. Many
illustrative examples and exercises are provided in order to facilitate
classroom teaching. A familiarity is assumed to the outlier detection
problem and also to generic problem of ensemble analysis in
classification. This is because many of the ensemble methods discussed
in this book are adaptations from their counterparts in the
classification domain. Some techniques explained in this book, such as
wagging, randomized feature weighting, and geometric subsampling,
provide new insights that are not available elsewhere. Also included is
an analysis of the performance of various types of base detectors and
their relative effectiveness. The book is valuable for researchers and
practitioners for leveraging ensemble methods into optimal algorithmic
design.
