Open Access Open Access  Restricted Access Subscription or Fee Access

Weather Forecasting using Incremental K-Means Clustering

Sanjay Chakraborty, N.K. Nagwani, Lopamudra Dey


Clustering has wide application areas in several research fields. Clustering is a powerful tool which has been used in several forecasting works, such as time series forecasting, real time storm detection, flood forecasting and so on. In this paper, a generic methodology for weather forecasting is proposed by the help of incremental K-means clustering algorithm. Weather forecasting plays an important role in day to day applications. Weather forecasting of this paper is done based on the incremental air pollution database of west Bengal in the years of 2009 and 2010. This paper generally uses typical K-means clustering on the main air pollution database and a list of weather category will be developed based on the maximum mean values of the clusters.Now when the new data are coming, the incremental K-means is used to group those data into those clusters whose weather category has been already defined. Thus it builds up a strategy to predict the weather of the upcoming data of the upcoming days. This forecasting database is totally based on the weather of west Bengal and this forecasting methodology is developed to mitigating the impacts of air pollutions and launch focused modeling computations for prediction and forecasts of weather events. Here accuracy of this approach is also measured.


Air-Pollution Dataset, Clustering, Forecasting, Incremental K-Means, Weather.

Full Text:



S.Chakraborty and N.K.Nagwani, “Analysis and study of Incremental K-Means clustering algorithm”, Communication in Computer and Information Science, 1, Volume 169, High Performance Architecture and Grid Computing ( Springer Germany), Part 2, 2011, pp. 338-341.

S.Chakraborty and N.K.Nagwani, “ Performance evaluation of incremental K-means clustering algorithm” , IIJDWM, vol. 1, 2011,pp-54-59.

K. Mumtaz and Dr. K. Duraiswamy, “ A Novel Density based improved k-means Clustering Algorithm– Dbkmeans ”, IJCSE Vol. 02, No. 02, 2010, pp. 213-218,.

T. Kanungo and D.M. Mount, : “An Efficient k-Means Clustering Algorithm: Analysis and implentation”, IEEE transaction vol. 24, No. 7,2002.

C. Ordonez and E. Omiecinski, 2004: “An Efficient Disk- Based K- Means Clustering for Relational Databases, IEEE transaction on knowledge and Data Engineering,Vol.16,No.8

Aristidis Likasa ,Nikos Vlassis, Jakob J. Verbeek, ― “ The global k-means clustering algorithm” , the journal of the pattern recognition society, Pattern Recognition36 (2003) 2002, pp. 451-461.

Vipul Kedia, Vamsidhar Thummala, Kamalakar Karlapalem, “Time Series Forecasting through Clustering A

Case Study”, In Proceedings of COMAD'2005. pp.183~191,

Xiang Li, Beth Plale, Nithya Vijayakumar, Rahul Ramachandran, Sara Graves and Helen Conover, “Real-Time Storm Detection and Weather Forecast Activation through Data Mining and Events Processing”,Vol.1,No.2, DOI: 10.1007/s12145-008-0010-7,May 2008, pp. 49-57.

Dr. S. Santhosh Baboo and I.Kadar Shereef, “An Efficient Weather Forecasting System using Artificial Neural Network ”, International Journal of Environmental Science and Development, Vol. 1, No. 4, ISSN: 2010-0264, October, 2010.

Godfrey C. Onwubolu, Peter Buryan2, Sitaram Garimella, Visagaperuman Ramachandran, Viti Buadromoand Ajith Abraham, “Self-organizing Data Mining for Weather Forecasting”, IADIS European Conference Data Mining, ISBN: 978-972-8924-40-9, 2007.

“Air Pollution”, Wikipedia free encyclopaedia

“Air pollution and Climate change”, published by Science for Environment Policy, European Commission, issue 24, Nov-2010.

Hans Eerens, Markus Amann, Jelle G. van Minnen, “The ETC- ACC framework for base-line scenario development in the context of integrated assessment for air pollution and climate change”, European Topic Centre on Air and Climate Change(ETC/ACC), 2002.

M.Khannan, S. Prabha karan and P. Ramachandran, “Rainfall Forecasting Using Data Mining Technique, International Journal of Engineering and Technology, Vol.2 (6), 2010, pp. 397-401.

Z. Jan, M. Abrar, S. Bashir, and A. M. Mirza, “Seasonal to Inter-annual Climate Prediction Using Data Mining KNN Technique (Springer Verlag), IMTIC 2008, CCIS 20, 2008, pp. 40–51.


  • There are currently no refbacks.

Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.