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A Novel Approach for Enhancing Clustering Technique using Knowledge-based to Plan the Social Infrastructure Services

by Hesham A. Salman, Lamiaa Fattouh Ibrahim, Zaki Taha
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 77 - Number 17
Year of Publication: 2013
Authors: Hesham A. Salman, Lamiaa Fattouh Ibrahim, Zaki Taha
10.5120/13619-1438

Hesham A. Salman, Lamiaa Fattouh Ibrahim, Zaki Taha . A Novel Approach for Enhancing Clustering Technique using Knowledge-based to Plan the Social Infrastructure Services. International Journal of Computer Applications. 77, 17 ( September 2013), 45-50. DOI=10.5120/13619-1438

@article{ 10.5120/13619-1438,
author = { Hesham A. Salman, Lamiaa Fattouh Ibrahim, Zaki Taha },
title = { A Novel Approach for Enhancing Clustering Technique using Knowledge-based to Plan the Social Infrastructure Services },
journal = { International Journal of Computer Applications },
issue_date = { September 2013 },
volume = { 77 },
number = { 17 },
month = { September },
year = { 2013 },
issn = { 0975-8887 },
pages = { 45-50 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume77/number17/13619-1438/ },
doi = { 10.5120/13619-1438 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:50:20.646794+05:30
%A Hesham A. Salman
%A Lamiaa Fattouh Ibrahim
%A Zaki Taha
%T A Novel Approach for Enhancing Clustering Technique using Knowledge-based to Plan the Social Infrastructure Services
%J International Journal of Computer Applications
%@ 0975-8887
%V 77
%N 17
%P 45-50
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This paper deals with social infrastructure planning problems to determine the location of the facilities of social infrastructure network and the layout. Each user must be assigned to the closest facility to be economically viable. The objective is how to make the accessibility to facilities maximum (i. e. , to minimize the distance which the users traveled to reach the facilities). In this paper, we study the problem of clustering in the presence of obstacles to locate the public service facility. In this article we present a new algorithm in data mining in the presence of obstacles. Minimum pre-specified level of demand must served by each facility. The objective is to maximize the accessibility of the facilities this means also to minimize the distance travelled by users to reach the facilities. CSPOD-DBSC algorithm (Clustering with short path Obstructed Distance - Density-Based Spatial Clustering) is developed. Obstructed short path distance calculated in this algorithm by using Density-based clustering algorithm and Dijkstra algorithm. A case study involving the location of schools in districts of Mecca in Saudi Arabia is used to illustrate the application of this algorithm.

References
  1. Bigotte, J. F. , Antunes, A. P. 2007. Social Infrastructure Planning: A Location Model and Solution Methods, Computer-Aided Civil and Infrastructure Engineering 22 (2007) 570–583.
  2. Kaufman L. , and Rousseeuw, P, 1990. Finding groups in Data: an Introduction to cluster, John Wiley & Sons.
  3. Han, J. , Kamber, M. , and Tung, A. 2001. Spatial Clustering Methods in data mining: A Survey, Geographic Data Mining and Knowledge Discovery.
  4. Bradly, P. , Fayyad, U. , and Reina, C. 1998. Scaling clustering algorithms to large databases. In proc. 1998 Int. Conf. Knoweldge Discovery and Data mining.
  5. Zhang, T. , Ramakrishnan, R. , and Livny, M. 1996. BIRCH: an efficient data clustering method for very large.
  6. Guha, S. , Rastogi, R. , and Shim, K. 1998. Cure : An efficient clustering algorithm for large databases. In Proc. 1998 ACM-SIGMOD Int. Conf. Management of Data (SIGMOD'98).
  7. Ester, M. , Kriegel, H. , Sander, J. and Xu, X. 1996. A density based algorithm for discovering clusters in large spatial databases. In Proc. 1996 Inc. Conf. Knowledge discovery and Data mining (KDD'96).
  8. Ankerst, M. , Breunig, M. , kriegel, H. and Sander, J. , 1999. OPTICS: Ordering points to identify the clustering structure. In Proc. 1999 ACM-SIGMOD Int. Conf. Management of data ( SIGMOD'96).
  9. Hinneburg, A. and Keim, A. 1998. An efficient approach to clustering in large multimedia databases with noise. In Proc. 1998 Int. Conf. . Knowledge discovery and Data mining (KDD'98).
  10. Ibrahim, L. F. 2011. Enhancing Clustering Network Planning Algorithm in the Presence of Obstacles, KDIR International Conference on Knowledge Discovery and Information Retrieval, KDIR is part of IC3K, the International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, Paris, France 26- 29 October 2011.
  11. Wang, W. , Yang, J. , and Muntz, R. 1997. STING: A statistical information grid approach to spatial data mining. In Proc. 1997 Int. Conf. Very Large Data Bases (VLDB'97).
  12. Sheikholeslami, G. , Chatterjee, S. and Zhang, A. 1998. Wave Cluster : A multi- resolution clustering approach for very large spatial databases. In Proc. 1997 Int. Conf. Very Large Data Bases ( VLDB'97).
  13. Agrawal, R. , Gehrke, J. , Gunopulos, D. , Raghavan,P. 1998. Automatic subspace clustering of high dimensional data for data mining application. In Proc. 1998 ACM-SIGMOD Int. Conf. Management of Data (SIGMOD'98).
  14. Kohonen, T. 1982. Self organized formation of topologically correct feature map. Biological Cybernetics.
  15. Nanopoulosl , A. , Theodoridis , Y. , Manolopoulos, Y. 2001. C2P: Clustering based on Closest Pairs. Proceedings of the 27th International Conference on Very Large Data Bases, p. 331-340, September 11-14.
  16. Tan, P. , Steinback, M. , and Kumar, V. 2006. Introduction to Data Mining. Addison Wesley.
  17. Salman, H. A. , Ibrahim L. F. , Fayed Z. 2013. Enhancing Clustering Technique Using Knowledge-Based to Plan The Social Infrastructure Services. 5th International Conference on Agents and Artificial Intelligence (ICAART 2013), Bacelona, Spain, 15-18 February 2013.
  18. Salman, H. A. , Ibrahim L. F. , Fayed Z. 2013. Enhancing Clustering Technique to Plan Social Infrastructure Services. ISMS2013 Fourth International conference on Intelligent Systems, Modelling and Simulation, Bangkok (Thailand) 29-31 January 2013, IEEE Xplore Press.
  19. Cornuejols, G. , Nemhauser, G. L. &Wolsey, L. A. 1990. The uncapacitated facility location problem, in P. B. Mirchandani and R. L. Francis (eds. ), Discrete Location Theory, JohnWiley & Sons, New York, pp. 119–71.
  20. Mirchandani, P. B. 1990. The p-median problem and generalizations, in P. B. Mirchandani and R. L. Francis, (eds. ), Discrete Location Theory, John Wiley & Sons, New York, pp. 55–117.
  21. Lorena, L. A. N. , Senne, E. L. F. 2004. A column generation approach to capacitated p-median problems, Computers and Operations Research, 31(6), 863–76.
  22. Ceselli, A. ,Righini,G. 2005. A branch-and-price algorithm for the capacitated p-median problem, Networks, 45(3), 125–42.
  23. Diaz, J. A. , Fernandez, E. 2006. Hybrid scatter search and path relinking for the capacitated p-median problem, European Journal of Operational Research, 169(2), 570– 85.
  24. Bigotte, J. F. , Antunes, A. P. 2007. Social Infrastructure Planning: A Location Model and Solution Methods, Computer-Aided Civil and Infrastructure Engineering 22 (2007) 570–583.
Index Terms

Computer Science
Information Sciences

Keywords

Clustering algorithm infrastructure city planning Spatial Clustering algorithm Urban Planning public service facility

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