Outlier Detection Techniques over Streaming Data in Data Mining: A Research Perspective
Prakash Chandore1, Prashant Chatur2
1Prakash Chandore, Department of Computer Science & Engineering, Govt. College of Engineering Amravati, Amravati (Maharashtra), India.
2Dr. Prashant Chatur, Department of Computer Science & Engineering, Govt. College of Engineering Amravati, Amravati (Maharashtra), India.
Manuscript received on 21 March 2013 | Revised Manuscript received on 28 March 2013 | Manuscript published on 30 March 2013 | PP: 157-162 | Volume-2 Issue-1, March 2013 | Retrieval Number: A0546032113/2013©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Data mining is extensively studied field of research area; where most of the work is emphasized over knowledge discovery. Data stream mining is active research area of data mining. A data stream is a massive sequence of data elements continuously generated at a rapid rate. In streaming huge amount of data continuously inserted and queried such data has very large database. Streaming data analysis has recently attracted attention over data stream rather than mining large data sets in data mining community. Outlier Detection as branch of data mining has many applications in data stream analysis and requires more attention. Finding and removing outlier over data stream is very important aspect in data mining. Detecting outlier and analyzing data stream for large dataset we can consider two main groups where one group refers to data stream and data mining techniques and second group refers to different efficient algorithm to mine data stream. Detecting outliers and analyzing large data sets can lead to discovery of unexpected knowledge in area such as fraud detection, telecommunication, web logs, and web document and click stream, etc. In this paper we try to clarify problem with detecting outlier over Dynamic data stream and specific techniques used for detecting outlier over streaming data in data mining.
Keywords: About Four Key Words or Phrases in Alphabetical Order, Separated by Commas.
Scope of the Article: Data Mining