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Proceeding of
as
A Special Issue of
International Journal of Computer Science and
Applications
(ISSN:0974-1011)
Advisory Committee
Dr. G. R. Bamnote
(Dean, Faculty of Engg, SGBAU Amravati)
Dr. B. E. Narkhede
(Vice President IIIE, Mumbai)
Dr. Md. Mamun Habib
(University Utara Malaysia (UUM, Malaysia)
Dr. C. R. Patil
(Prof. PRMIT&R Badnera, Amravati)
Dr. T. R. Deshmukh
(Prof. PRMIT&R Badnera, Amravati)
Dr. W. Z. Gandhare
(Principal, Govt. College of Engineering Amravati)
Dr. D. N. Kyatanavar
(Principal, SRESCOE Kopargaon)
Dr. U. Pendharkar
(Professor, Government Engineering College, Ujjain)
Dr. Ajit Thete
(Director, Centre for Development of Leadership in
Education Pvt Ltd, Aurangabad)
Technical Committee
Dr. M. T. Datar
Dr. S. K. Garg
Dr. Shrikaant Kulkarni
Shri. D. N. Patil
Dr. A. W. Kolte
Prof. Ajitabh Pateriya
Prof. P. K. Patil
Prof. S.N. Khachane
Prof. Parag Chourey
Prof. B.K.Chaudhari
Prof. N.A. Kharche
Prof. R.M. Choudhari
Prof. R. B. Pandhare
Prof. A.P. Jadhao
Prof. S.B. Jadhav
Prof. Santosh Raikar
Prof. Y.P. Sushir
Prof. B. M. Tayde
Editor
Prof. K. H. Walse
Research Publications, India
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IJCSA ISSN: 0974-1011 (Online)
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Title: |
Detection of Rare Patterns in Climate Change Using Data Mining
Techniques |
Author: |
Mr. Parag N. Kolhe, Mr. Rahul M.Ugale and Miss Shraddha V
Shingne |
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Abstract |
Today the volume of data has been enormously increasing as a
result of advances in data generation, collection and storage
technologies. The effect of climate prediction on society,
business, agriculture and almost all aspects of human life,
force the scientist to give proper attention to the matter.
Weather is a continuous, data-intensive, multidimensional,
dynamic process that makes weather forecasting a formidable
challenge. The prediction of rare events is a pressing
scientific problem. We primarily concentrate to identify
weather patterns in the long term while consistent with global
climate change on weather patterns, identify rare/outlying
patterns that coincide with rare events data mining.
This paper propose an adaptive clustering pattern detection
method for the detection of rare patterns in climate change
using data mining techniques which uses k-means algorithm
where an open number of states as clusters to accommodate the
dynamic temporarily of data. By adding adaptive clustering
property as a global restriction, the granular size of the
clusters is determined for optimal performance. The global
modeling result is presented which provides a base of data
mining tasks.
The metrological variables longitude, latitude, mer wind, zone
wind, humidity, air temperature and sea surface temperature
are analyzed to detect climate change patterns in this study.
The result depicts different patterns of climate in the form
of histogram based on the records of metrological variables of
NOOA. Different distance measures are applied between the
centers of the clusters formed for testing the sensitivity of
the method. The robustness of our method is demonstrated by
the results. Our method of detecting rare patterns in climate
change will be very useful for weather and metrological
research focusing on the trends in weather and the consequent
changes. Adaptive clustering method uses an open number of
states as clusters to accommodate the dynamic temporarily of
data.
By adding adaptive clustering property as a global
restriction, the granular size of the clusters is determined
for optimal performance. The global modeling result is
presented which provides a base of data mining tasks. This
adaptive climate change pattern detection algorithm will be
proven to be of potential use for climatic and meteorological
research as well as research focusing on temporal trends in
weather and the consequent changes. |
©2015 International Journal of
Computer Science and Applications
Published by Research
Publications, India |
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