Open AccessA Preliminary Performance Evaluation of K-means, KNN and EM Unsupervised Machine Learning Methods for Network Flow Classification
Author(s) -
Alhamza Alalousi,
Rozmie Razif,
Mosleh M. Abualhaj,
Mohammed Anbar,
Shahrul Nizam
Publication year - 2016
Publication title -
international journal of electrical and computer engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.277
H-Index - 22
ISSN - 2088-8708
DOI - 10.11591/ijece.v6i2.pp778-784
Subject(s) - computer science , artificial intelligence , k nearest neighbors algorithm , unsupervised learning , pattern recognition (psychology) , machine learning , class (philosophy) , maximization , data mining , mathematics , mathematical optimization
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.