  From the past decade outlier detection has been in use. Detection of outliers
is an emerging topic and is having robust applications in medical sciences and
pharmaceutical sciences. Outlier detection is used to detect anomalous
behaviour of data. Typical problems in Bioinformatics can be addressed by
outlier detection. A computationally fast method for detecting outliers is
shown, that is particularly effective in high dimensions. PrCmpOut algorithm
make use of simple properties of principal components to detect outliers in the
transformed space, leading to significant computational advantages for high
dimensional data. This procedure requires considerably less computational time
than existing methods for outlier detection. The properties of this estimator
(Outlier error rate (FN), Non-Outlier error rate(FP) and computational costs)
are analyzed and compared with those of other robust estimators described in
the literature through simulation studies. Numerical evidence based Oxazolines
and Oxazoles molecular descriptor dataset shows that the proposed method
performs well in a variety of situations of practical interest. It is thus a
valuable companion to the existing outlier detection methods.
