Earticle

초록

영어

Data mining is involved in procedures by which patterns are extracted from data. This process has become increasingly important to map data patterns to useful information that can be used to predict future traffic analyses. Other areas were data mining can be used include: fraud detection, marketing, congestion control, and network expansion consideration.
Data mining involves capturing and gathering random data from the flow of information passing through a certain trunk or node and from a statistical point of view, it can only be meaningful if enough samples are gathered. The results achieved from a proper deployment of a data mining method provide valuable insights to how busy a node is, the average oneway and end-to-end delays, and the average size of the packets.. This paper contains the results from capturing live wired traffic and analyzing the statistical values.

목차

Abstract
 1. Introduction
 2. Data mining parameters
  2.1. Packet size
  2.2. Flow Duration
  2.3. Confidence Interval (CI)
 3. Real-time data measures
  3.1. Wired data analysis
  3.2. Scatter plots in data mining
  3.3. Throughput graphs
  3.4. Confidence Interval (CI) calculation
  3.5. TCP traffic versus UDP traffic
 4. Conclusions
 5. References

키워드

저자

• Sasan Adibi [ ECE Department, University of Waterloo ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Advanced Science and Technology

• 간기

  월간

• pISSN

  2005-4238

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605