Development of a Decision Support System for Urban Planning by Using K-means ++ Algorithm


  • Hussein Ali Salih Department of Computer Systems, Technical Institute- Suwaira, Middle Technical University, Iraq. Faculty of Automatic Control and Computer, University Politehnica of Bucharest, Romania
  • Ahmed Shihab Ahmed Department of Basic Sciences, College of Nursing, University of Baghdad, IRAQ
  • Jalal Qais Jameel College of Medicine, Mustansiriyah University.



Decision Making, Decision Support System –Urban Planning, Data Mining, K-Means .


This article depicts a decision support system (DSS) devoted to the coordinated administration of urban frameworks. This framework defines the information and related treatments normal to a few civil managers and characterizes the necessities and functionalities of the PC devices created to enhance the conveyance, execution, and coordination of metropolitan administrations to the populace. The cooperative framework called Decision Support System for Urban Planning (DSS-UP) is made out of a universal planning and coordination framework. So, it helps the decision-making process, a DSS was created as a learning-based framework gave derivation components that empower urban architect to settle on key decisions as far as specialized meditations on civil foundations. The learning-based framework stores experts_ information and additionally answers for past issues. Preparatory execution comes about demonstrate that DSS-UP viably and effectively underpins the decision-making process identified with overseeing urban foundations by using K-means++ data mining algorithm.


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Author Biography

  • Ahmed Shihab Ahmed, Department of Basic Sciences, College of Nursing, University of Baghdad, IRAQ
    Department of Basic Sciences, College of Nursing, University of Baghdad, IRAQ


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Original Article

How to Cite

H. A. Salih, A. S. Ahmed, and J. Q. Jameel, “Development of a Decision Support System for Urban Planning by Using K-means ++ Algorithm”, Al-Mustansiriyah Journal of Science, vol. 31, no. 3, pp. 78–88, Aug. 2020, doi: 10.23851/mjs.v31i3.721.

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