Outlier detection in streaming data a research perspective

• Published in: 2014 International Conference on Parallel, Distributed and Grid Computing pp. 429 - 432
• Main Authors: Chugh, Neeraj, Chugh, Mitali, Agarwal, Alok
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.12.2014
• Subjects: Adaptation models; Biological system modeling; Data analysis; Data mining; Data models; data stream mining; Distributed databases; Outliers
• ISBN: 1479976822; 9781479976829
• DOI: 10.1109/PDGC.2014.7030784

Data mining is a system that brings up the light to hidden and valuable information from the data and the facts revealed by data mining which were previously not known, theoretically useful, and of high quality. Data mining offers a means by which we can explores the knowledge in database. Data stream mining and finding outliers are dynamic research areas of data mining. It is thought that `data stream mining and outlier detection' research has drastically expanded the range of data analysis and will have profound impact on data mining methodologies and applications in the long run. However, there are still some difficult research problem that are to be answered before data stream mining and outlier detection can declare a keystone approach in data mining applications. The aim of this work is to simplify problems related to detecting outlier over dynamic data stream and exploring explicit techniques used for detecting outlier over streaming data in data mining presented by researchers in recent years and also to look at the future trends.