TY  - JOUR
T1  - An Efficient Cluster based Outlier Detection Algorithm
AU - Priya, M. AU - Karthikeyan, M. 
JO  - Journal of Engineering and Applied Sciences
VL  - 14
IS  - 23
SP  - 8699
EP  - 8704
PY  - 2019
DA  - 2001/08/19
SN  - 1816-949x
DO  - jeasci.2019.8699.8704
UR  - https://makhillpublications.co/view-article.php?doi=jeasci.2019.8699.8704
KW  - Data mining
KW  -outlier detection
KW  -cluster
KW  -outlier clusters
KW  -mutual nearest neighbor
KW  -fault diagnosis
AB  - Outlier analysis is becoming an important technique in data mining whose task is to identifying the
data objects that are completely different from the majority of all objects. Outlier detection is necessary and
useful with numerous applications in many fields like medical, fraud detection, fault diagnosis in machines, etc.
In this study, we tend to propose a cluster based outlier detection algorithm which can be fulfilled in two
stages. In the first stage we construct cluster using mutual nearest neighbor graph clustering algorithm. In the
second stage we find the cluster outlier factor based on size of each cluster. The concept is to find outlier value
of object and outlier clusters are extended to the formation of cluster. This algorithm is used to identify objects
must confided as outlier object and outlier clusters in a database. This algorithm is based on the thought of
mutual nearest neighbor graph clustering. The proposed algorithm can be used to identify the outlier value
factor in the database and to detect the outliers and outlier clusters efficiently. The simulation result shows that
the proposed method yields better results in outlier detection.
ER  -