DETEKSI DATA PENCILAN MENGGUNAKAN K_MEANS CLUSTERING

Publication Type:

Journal Article

Source:

Jurnal Teknologi, Volume 3, Number 1 (2010)

Keywords:

clustering, K_means, outlier

Abstract:

Outlier detection is an extremely important task in a wide variety of application e g frand detection, identifying computer network intrusions and bottleneck, credit card fraud, criminal activities in e-commerce. In this paper we are concerned with outlier detection using K_means clustering. In this case number of cluster, is regarded as parameter and incrementally added until we get small cluster and regarded as a collection of outlier. Finally it is illustrated how this method work on sets of data.

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