Data mining is a highly researched area in the today’s world as data is crucial part of many application, due
to which many researchers express their interest in this domain. As there arises a need to process large
dataset which imposes different challenges for researchers. To have a data which is free from a noisy
attributes , known as a filtered data , is of much important to gain accuracy in a result sets. For that , finding
and eliminate the noisy objects has gained a much more importance. An object that does not follow the
footprints of usual data object is called outliers. Outlier detection process is used in numerous applications
like fraud detection, intrusion detection system, tracking environmental activities, healthcare diagnosis.
Numbers of approaches are used in the process of detection of outlier. Most approaches focuses to use
Cluster-based and Distance based approach (i.e. using K- Means algorithm and Euclidian distance) for
outlier detection in data sets which help them to create a group of similar elements or cluster of data points.
Clustering techniques are highly useful for grouping similar data items from data sets and after that by
applying distance based calculations, detection of outlier is done, so they are called cluster-based outlier
detection. K- Means and Euclidian distance are the most common and popular algorithm for clustering and
outlier detection process due to its simplicity and efficiency. Different application areas of outlier detection
are discussed in this paper.