DocumentCode
1974612
Title
Detecting Deviants over Data Streams
Author
Wei, Zhang ; Zhang Wei
Author_Institution
Sch. of Comput. Sci., Huazhong Univ. of Sci. & Technol., Wuhan, China
fYear
2010
fDate
20-22 Aug. 2010
Firstpage
1
Lastpage
3
Abstract
Identifying outliers is a difficult thing in data mining. We adopt the notion of deviants for outliers in data streams. Deviants are data set whose removal from the data sequence over data streams lead to sum of error SSE minimize. We present DDA algorithm to detect deviants over massive data streams. With this algorithm the histogram can more accurately determine the deviants and greatly reduce error.
Keywords
data analysis; data mining; database management systems; least squares approximations; DDA algorithm; data mining; data sequence; data streams; deviant detection; Algorithm design and analysis; Computer science; Data mining; Educational institutions; Heuristic algorithms; Histograms; Time series analysis;
fLanguage
English
Publisher
ieee
Conference_Titel
Internet Technology and Applications, 2010 International Conference on
Conference_Location
Wuhan
Print_ISBN
978-1-4244-5142-5
Electronic_ISBN
978-1-4244-5143-2
Type
conf
DOI
10.1109/ITAPP.2010.5566128
Filename
5566128
Link To Document