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Mosleh AbuAlhaj
Dept. of Network and Information Security, Faculty of Information Technology, Al-Ahliyya Amman University
Computer Science & IT Electrical & Electronics Engineering

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A Preliminary Performance Evaluation of K-means, KNN and EM Unsupervised Machine Learning Methods for Network Flow Classification Alhamza Alalousi; Rozmie Razif; Mosleh AbuAlhaj; Mohammed Anbar; Shahrul Nizam
International Journal of Electrical and Computer Engineering (IJECE) Vol 6, No 2: April 2016
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (186.691 KB) | DOI: 10.11591/ijece.v6i2.pp778-784

Abstract

Unsupervised leaning is a popular method for classify unlabeled dataset i.e. without prior knowledge about data class. Many of unsupervised learning are used to inspect and classify network flow. This paper presents in-deep study for three unsupervised classifiers, namely: K-means, K-nearest neighbor and Expectation maximization. The methodologies and how it’s employed to classify network flow are elaborated in details. The three classifiers are evaluated using three significant metrics, which are classification accuracy, classification speed and memory consuming. The K-nearest neighbor introduce better results for accuracy and memory; while K-means announce lowest processing time.
Co-Authors Alhamza Alalousi Mohammed Anbar Rozmie Razif Shahrul Nizam