Removing of Anomalies in High Dimensional Data Multi Clustering Structure

B  Sathish Kumar  and Kannan Subramanian

doi:10.17485/ijst/2015/v8i32/87510

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Removing of Anomalies in High Dimensional Data Multi Clustering Structure

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Indian Journal of Science and Technology

DOI: 10.17485/ijst/2015/v8i32/87510

Year: 2015, Volume: 8, Issue: 32, Pages: 1-4

Original Article

Removing of Anomalies in High Dimensional Data Multi Clustering Structure

B. Sathish Kumar¹ and Kannan Subramanian^2*

¹Master of Computer Application, Jerusalem College of Engineering, , Chennai - 600 100, Tamil Nadu, India; [email protected]
^2* Master of Computer Application, Bharath University, Chennai - 600 073, Tamil Nadu, India; [email protected]

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

In data mining anomalies are one of the main threats for efficient information retrieval from databases. Anomalies are also known as anomalies. Mining of anomalies from the normal data is very important and scope of this is very high. Anomaly detection can be found in applications such as credit card fraud detection, intrusion and insider threat detection in cybersecurity, detection of fault, or malignant diagnosis. Anomalous data present in database is harmful for the processing of information and usage of that information. Viscous data contain erroneous information and it may contain dangerous code for carking the whole system where it is stored.