Classification via k-means clustering and distance-based outlier detection

Author

Songma, S. ; Chimphlee, W. ; Maichalernnukul, Kiattisak ; Sanguansat, Parinya

Author_Institution

Fac. of Inf. Technol., Rangsit Univ., Phathumthani, Thailand

fYear

2012

fDate

21-23 Nov. 2012

Firstpage

125

Lastpage

128

Abstract

We propose a two-phase classification method. Specifically, in the first phase, a set of patterns (data) are clustered by the k-means algorithm. In the second phase, outliers are constructed by a distance-based technique and a class label is assigned to each pattern. The Knowledge Discovery Databases (KDD) Cup 1999 data set, which has been utilized extensively for development of intrusion detection systems, is used in our experiment. The results show that the proposed method is effective in intrusion detection.

Keywords

data mining; pattern classification; pattern clustering; security of data; statistical analysis; KDD Cup 1999 data set; class label; distance-based outlier detection; distance-based technique; intrusion detection systems; k-means algorithm; k-means clustering; knowledge discovery databases; two-phase classification method; Classification algorithms; Clustering algorithms; Databases; Educational institutions; Intrusion detection; Support vector machines; Training; Classification; KDD Cup 1999 data set; intrusion detection; k-means; outlier detection;

fLanguage

English

Publisher

ieee

Conference_Titel

ICT and Knowledge Engineering (ICT & Knowledge Engineering), 2012 10th International Conference on

Conference_Location

Bangkok

ISSN

2157-0981

Print_ISBN

978-1-4673-2316-1

Type

conf

DOI

10.1109/ICTKE.2012.6408540

Filename

6408540