Approach of outlier mining based on high dimension sparse clustering

Author

Baoguo, Chen

Author_Institution

Dept. of Math., Huainan Normal Univ., Huainan, China

fYear

2011

fDate

8-10 Aug. 2011

Firstpage

215

Lastpage

217

Abstract

To solve the problem that CABOSFV is only effective to high dimension sparse clustering problems of two-value attributes, this paper presents an improved high dimension sparse clustering approach by defining “absolute attribute distance”. The experimental results show that this method is efficient in outlier mining, and easy to calculate and program, so it is a more practical method.

Keywords

data mining; pattern clustering; statistical analysis; CABOSFV algorithm; absolute attribute distance; clustering algorithm based on sparse feature vector; high dimension sparse clustering; outlier mining; Clustering algorithms; Communications technology; Credit cards; Data mining; Indexes; Presses; CABOSFV; High dimension; Outlier mining; Sparse clustering;

fLanguage

English

Publisher

ieee

Conference_Titel

Artificial Intelligence, Management Science and Electronic Commerce (AIMSEC), 2011 2nd International Conference on

Conference_Location

Deng Leng

Print_ISBN

978-1-4577-0535-9

Type

conf

DOI

10.1109/AIMSEC.2011.6010420

Filename

6010420

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=3170632