Clustering of Noisy Regions (CNR)—A GIS Anchored Technique for Clustering on Raster Map
In this proposed work, a GIS anchored system has been approached, which initially takes as input, a digitized map, generally of a very large region, with the population of the common mass in different wards/areas fed as associated data and finally it suggests the most suitable locations for constructing a number of utility service centers. This target can be achieved by formation of clusters of the wards, where only the adjacent regions are the part of any particular cluster. Particularly, for the third world countries like India, not only the heavily populated wards, but also some small populations such as small slum areas, generally locating outskirts of the major cities, which could be viewed as noise due to their small populations, would also have to be considered for the purpose. One such popular and well-accepted clustering technique handling noise is DBSCAN Ester et al. (A density-based algorithm for discovering clusters in large spatial databases with noise, 1996). But, the major demerit of DBSCAN algorithm is that it cannot take as input, the number of clusters to be generated. This is quiet impractical, as because the number of centers to be constructed is decided beforehand, by some planning committee. Moreover, DBSCAN simply omits the noise areas. But, with a motto to provide equal opportunity for every citizen, care should be taken for all parts of the population. The proposed technique takes a step forward towards the solution.
KeywordsGIS Utility service stations Clusters Associated data Digitized map DBSCAN
The authors express a deep sense of gratitude to the Department of Computer Science, Barrackpore Rastraguru Surendranath College, Kolkata-700 120, India and Department of Computer Science and Engineering, University of Kalyani for providing necessary infrastructural support for the work and to the UGC for financial support under Minor Research Project scheme (sanction number F. PSW-180/11-12 ERO, dated 25.01.2012).
- 1.Ester, M., Kriegel, H.-P., Sander, J., Xu, X.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining (KDD-96), Montreal, Canada (1996)Google Scholar
- 2.Mandal, J.K., Chakraborty, A., Chakrabarti, A.K.: A GIS based approach of clustering for new facility areas in a digitized map. In: Proceedings of the International Conference on Eco-friendly Computing and Communication Systems (ICECCS 2012), CCIS 305 ©, pp. 398–405. Springer, Berlin, 9–11 Aug 2012Google Scholar
- 3.Raskutti, B., Telstra, C.L.: An evaluation of criteria for measuring the quality of clusters. In: IJCAI Proceedings of the 16th International Joint Conference on Artificial Intelligence, vol. 2, pp. 905–910, Morgan Kaufman Publishers Inc., San Francisco (1999)Google Scholar
- 4.Chakraborty, A., Mandal, J.K., Chandrabanshi, S.B., Sarkar, S.: A GIS anchored system for selection of utility service stations trough k-means method of clustering. In: Proceedings of the Second International Conference on Computing and Systems (ICCS-2013), ISBN 978-9-35-134273-1, pp. 244–251. McGraw Hill Education (India) Private Limited, 21–22 September, 2013Google Scholar
- 5.http://www.zetcode.com/tutorials/javaswingtutorial. Accessed 05 Aug 2014
- 6.Bezdek, J.C.: Pattern Recognition with Fuzzy Objective Function Algorithms. Plenum Press, New York (1981)Google Scholar
- 7.Chakraborty, A., Mandal, J.K., Chandrabanshi, S.B., Sarkar, S.: A GIS anchored system for selection of utility service stations trough hierarchical clustering. In: Proceedings of the First International Conference on Computational Intelligence: Modeling, Techniques and Applications (CIMTA-2013), ISBN 978-93-5126-672-3, Procedia Technology, Elsevier, 27–28 Sept 2013Google Scholar