Active Storage Networks for Accelerating K-Means Data Clustering

Singaraju, Janardhan; Chandy, John A.

doi:10.1007/978-3-642-19475-7_12

Cited by 1 publication

(1 citation statement)

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“…However, the proposed method requires large training set (10 instance) to achieve high accuracy. On the other hand, implementations of nonincremental -means algorithm in FPGA are more common, for example, [29][30][31][32]. However, implementation of Euclidean distance based -means algorithm on hardware consumes high hardware resources.…”

Section: Related Workmentioning

confidence: 99%

Online Incremental Learning for High Bandwidth Network Traffic Classification

Loo

Bassi

Marsono

2016

Applied Computational Intelligence and Soft Computing

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Data stream mining techniques are able to classify evolving data streams such as network traffic in the presence of concept drift. In order to classify high bandwidth network traffic in real-time, data stream mining classifiers need to be implemented on reconfigurable high throughput platform, such as Field Programmable Gate Array (FPGA). This paper proposes an algorithm for online network traffic classification based on the concept of incrementalk-means clustering to continuously learn from both labeled and unlabeled flow instances. Two distance measures for incrementalk-means (Euclidean and Manhattan) distance are analyzed to measure their impact on the network traffic classification in the presence of concept drift. The experimental results on real datasets show that the proposed algorithm exhibits consistency, up to 94% average accuracy for both distance measures, even in the presence of concept drifts. The proposed incrementalk-means classification using Manhattan distance can classify network traffic 3 times faster than Euclidean distance at 671 thousands flow instances per second.

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Section: Related Workmentioning

confidence: 99%