A Preliminary Performance Evaluation of K-means, KNN and EM Unsupervised Machine Learning Methods for Network Flow Classification
2016 ◽
Vol 6
(2)
◽
pp. 778
Keyword(s):
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.
2016 ◽
Vol 6
(2)
◽
pp. 778
◽
2021 ◽
Vol 2021
◽
pp. 1-7
STATISTICAL PREDICTION OF EMOTIONAL STATES BY PHYSIOLOGICAL SIGNALS WITH MANOVA AND MACHINE LEARNING
2012 ◽
Vol 26
(04)
◽
pp. 1250008
◽