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- W2181458082 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." @default.
- W2181458082 created "2016-06-24" @default.
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- W2181458082 date "2013-01-01" @default.
- W2181458082 modified "2023-09-26" @default.
- W2181458082 title "Outlier Detection Techniques over Streaming Data in Data Mining: A Research Perspective" @default.
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