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Reseach Article

KNN Technique for Analysis and Prediction of Temperature and Humidity Data

by Sagar S. Badhiye, Nilesh U. Sambhe, P. N. Chatur
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 61 - Number 14
Year of Publication: 2013
Authors: Sagar S. Badhiye, Nilesh U. Sambhe, P. N. Chatur
10.5120/9994-4847

Sagar S. Badhiye, Nilesh U. Sambhe, P. N. Chatur . KNN Technique for Analysis and Prediction of Temperature and Humidity Data. International Journal of Computer Applications. 61, 14 ( January 2013), 7-13. DOI=10.5120/9994-4847

@article{ 10.5120/9994-4847,
author = { Sagar S. Badhiye, Nilesh U. Sambhe, P. N. Chatur },
title = { KNN Technique for Analysis and Prediction of Temperature and Humidity Data },
journal = { International Journal of Computer Applications },
issue_date = { January 2013 },
volume = { 61 },
number = { 14 },
month = { January },
year = { 2013 },
issn = { 0975-8887 },
pages = { 7-13 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume61/number14/9994-4847/ },
doi = { 10.5120/9994-4847 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:09:05.331015+05:30
%A Sagar S. Badhiye
%A Nilesh U. Sambhe
%A P. N. Chatur
%T KNN Technique for Analysis and Prediction of Temperature and Humidity Data
%J International Journal of Computer Applications
%@ 0975-8887
%V 61
%N 14
%P 7-13
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The research investigates the data mining technique K-Nearest Neighbor resulting in a predictor for numerical series. The series experimented with come from the climatic data usually hard to forecast due to uncertainty. One approach of prediction is to spot patterns in the past, when it is known in advance what followed them and verify it on more recent data. If a pattern is followed by the same outcome frequently enough, it can be concluded that it is a genuine relationship. Because this approach does not assume any special knowledge or form of the regularities, the method is quite general applicable to other series not just climate. The research searches for an automated pattern spotting, it involves data mining technique K-Nearest Neighbor for prediction of temperature and humidity data for a specific region. The results of the research for temperature and humidity prediction by K-Nearest Neighbor were satisfactory as it is assumed that no forecasting technique can be 100 % accurate in prediction.

References
  1. Larose D. T. : Discovering Knowledge in Data: An Introduction to Data Mining, Wiley, Chichester 2005
  2. S. Kotsiantis and et. al. , "Using Data Mining Techniques for Estimating Minimum, Maximum and Average Daily Temperature Values", World Academy of Science, Engineering and Technology 2007 pp. 450-454
  3. Han J. , Kamber M. : Data Mining concepts and Techniques, Elsevier Science and Technology, Amsterdam 2006
  4. Cover T, Hart P (1967) "Nearest neighbor pattern classification". IEEE Trans Inform Theory Volume 13(1) pp. 21–27
  5. Badhiye S. S. , et. al. , 'Temperature and Humidity Data Analysis for Future Value Prediction using Clustering Technique: An Approach', International Journal of Emerging Technology and Advanced Engineering, 2(1), pp. 88-91, 2012.
Index Terms

Computer Science
Information Sciences

Keywords

Data Mining K-Nearest Neighbor Numerical Series