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Using KNN Method for Educational and Vocational Guidance

by Essaid El Haji, Abdellah Azmani, Mohamed El Harzli
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 100 - Number 12
Year of Publication: 2014
Authors: Essaid El Haji, Abdellah Azmani, Mohamed El Harzli
10.5120/17578-8335

Essaid El Haji, Abdellah Azmani, Mohamed El Harzli . Using KNN Method for Educational and Vocational Guidance. International Journal of Computer Applications. 100, 12 ( August 2014), 24-30. DOI=10.5120/17578-8335

@article{ 10.5120/17578-8335,
author = { Essaid El Haji, Abdellah Azmani, Mohamed El Harzli },
title = { Using KNN Method for Educational and Vocational Guidance },
journal = { International Journal of Computer Applications },
issue_date = { August 2014 },
volume = { 100 },
number = { 12 },
month = { August },
year = { 2014 },
issn = { 0975-8887 },
pages = { 24-30 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume100/number12/17578-8335/ },
doi = { 10.5120/17578-8335 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:29:48.101386+05:30
%A Essaid El Haji
%A Abdellah Azmani
%A Mohamed El Harzli
%T Using KNN Method for Educational and Vocational Guidance
%J International Journal of Computer Applications
%@ 0975-8887
%V 100
%N 12
%P 24-30
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This paper presents a decision support tool for educational and vocational guidance, based on the supervised classification method k-nearest neighbors (KNN). This method consists in determining, for each new observation to be classified, the list of nearest neighbors of the observations already classified. The use of the KNN method requires choosing a distance and the most classical one is the Euclidean distance. In the context of this work, two functions were tested to measure resemblance as far as similarity and dissimilarity are concerned.

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Index Terms

Computer Science
Information Sciences

Keywords

Educational and vocational guidance RIASEC pairing k-nearest neighbors similarity dissimilarity.

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