An Efficient Outlier Detection Using Amalgamation of Clustering and Attribute-Entropy Based Approach

Jasmine Natchial .Fa,*  Elango Parasuramanb and Taslina. Bc

An Efficient Outlier Detection Using Amalgamation of Clustering and Attribute-Entropy Based Approach

Authors :

Jasmine Natchial .F^a,*Elango Parasuraman^b and Taslina. B^c

Author Address :

^aPhD. Scholar, Bharathiar University, Coimbatore, India.
^bAssistant Professor, Department of IT, PKIET, Karaikal, India.
^cM.E Scholar, Dept of CSE, BCET, Karaikal, India.

*Corresponding author.

Abstract :

Many organizations today have more than very large databases that grow without limit at a rate of several million records per day. Mining these continuous data streams brings unique opportunities but also new challenges. Thus data stream has become a dynamic research area of Data mining. Data Stream Mining is the process of extracting knowledge structures from continuous, rapid data records. The data stream is motivated by emerging applications like Consumer click streams and Telephone records, Bulky set of web pages, Multimedia data and so on. Outlier detection in streaming data is a very challenging problem because of the reality that data streams cannot be scanned multiple times. The outliers may exert undue influence on the results of statistical analysis. So they should be identified using reliable detection methods prior to performing data analysis. The main objective of this proposed research work is to detect outliers using amalgamation technique where one or more techniques are combined for efficient anomaly detection.