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Selforganizology, 2016, 3(4): 121-139
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Article

An improved data clustering algorithm for outlier detection

Anant Agarwali, Arun Solanki
Department of Computer Science and Engineering, Gautam Buddha University, Greater Noida, India

Received 4 May 2016;Accepted 12 June 2016;Published 1 December 2016
IAEES

Abstract
Data mining is the extraction of hidden predictive information from large databases. This is a technology with potential to study and analyze useful information present in data. Data objects which do not usually fit into the general behavior of the data are termed as outliers. Outlier Detection in databases has numerous applications such as fraud detection, customized marketing, and the search for terrorism. By definition, outliers are rare occurrences and hence represent a small portion of the data. However, the use of Outlier Detection for various purposes is not an easy task. This research proposes a modified PAM for detecting outliers. The proposed technique has been implemented in JAVA. The results produced by the proposed technique are found better than existing technique in terms of outliers detected and time complexity.

Keywords data mining;outlier detection;clustering;pam;java.

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