Title :
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
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;
Conference_Titel :
ICT and Knowledge Engineering (ICT & Knowledge Engineering), 2012 10th International Conference on
Conference_Location :
Bangkok
Print_ISBN :
978-1-4673-2316-1
DOI :
10.1109/ICTKE.2012.6408540
