Abstract
With the availability of huge data sets in device fields like finances to weather, it becomes very important to quality analysis and interprets the results. In such scenario K-Means and DBSCAN clustering algorithms are used for effective data grouping to get insight into the hidden structure in the data. In this paper focus on the application of clustering to ocean data observations. An attempt is made to correlate the resulting clusters to the variability focused during cyclones.
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References
Data Mining: Concepts and Techniques (The Morgan Kaufmann Series in Data Management Systems) Hardcover – Import, 25 Jul 2011 by Jiawei Han (Author), Micheline Kamber (Author), Jian Pei Professor (Author).
Landman, W.A.: A canonical correlation analysis model to predict South African summer rainfall. NOAA Experimental Long-Lead Forecast Bulletin 4(4), 23–24 (1995).
Landman, W.A., Mason, S.J.: Forecasts of Near-Global Sea Surface Temperatures Using Canonical Correlation Analysis. Journal of Climate 14(18), 3819–3833 (2001).
Rogel, P., Maisonnave, E.: Using Jason-1 and Topex/Poseidon data for seasonal climate prediction studies. AVISO Altimetry Newsletter 8, 115–116 (2002).
White, A.B., Kumar, P., Tcheng, D.: A data mining approach for understanding control on climate induced inter-annual vegetation variability over the United State. Remote sensing of Environments 98, 1–20 (2005).
Basak, J., Sudarshan, A., Trivedi, D., Santhanam, M.S.: Weather Data Mining using Component Analysis. Journal of Machine Learning Research 5, 239–253 (2004).
Hsieh, W.W.: Nonlinear Canonical Correlation Analysis of the Tropical Pacific Climate Variability Using a Neural Network Approach. Journal of Climate 14(12), 2528–2539 (2001).
Hartigan, J.: Clustering Algorithms Wiley, New York (1975).
Jain, A.K, M.N. Murty, P.J. Flynn.: Data Clustering : A Review, ACM Computing Surveys, 31(3):264:323, September (1999).
M. Ester, H.P. Krigel, J. Sander, and X. Xu, “A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise, ”Proc.of the 2nd International Conference on Knowledge Discovery and Data Mining, Portland, WA, 1996, pp. 226–231.
B. Borah and D. K. Bhattacharyya, “An Improved Sampling- Based DBSCAN for Large Spatial Databases,” presented in the international Conference on Intelligent Sensing and Information Processing, Chennai, India, January 2004.
B. Borah and D. K. Bhaftacharyya, “A Clustering Technique using Density Difference,” IEEE - ICSCN 2007, MIT Campus, Anna University, Chennai, India. Feb. 22–24, 2007. pp. 585–588.
Jain, A. K, Dubes. R. C.: Algorithms for clustering Data Prentice Hall Advanced references Series, Prentice Hall, (1988).
Shai Shalev-Shwartz, and Shai Ben David: Understanding Machine Learning. From theory to Algorithms (2014).
Karsten Steinhaeuser, Nitesh V Chawla and Auroop R Ganguly: Comparing Predictive power in Climate Data: Clustering Matters (2014).
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Authors from ANITS would like to thank HOD ANITS for support. Authors from INCOIS would like to thank the Director INCOIS for providing all necessary facilities to carry out this work.
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Santosh Kumar, D.J., Vighneshwar, S.P., Mishra, T.K., Jampana, S.V. (2017). Time Series Analysis of Oceanographic Data Using Clustering Algorithms. In: Satapathy, S., Bhateja, V., Raju, K., Janakiramaiah, B. (eds) Computer Communication, Networking and Internet Security. Lecture Notes in Networks and Systems, vol 5. Springer, Singapore. https://doi.org/10.1007/978-981-10-3226-4_24
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DOI: https://doi.org/10.1007/978-981-10-3226-4_24
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