Authors:
Hesham A. Salman
1
;
Lamiaa Fattouh Ibrahim
2
and
Zaki Fayed
3
Affiliations:
1
Faculty of Computing and Information Technology and King Abdulaziz University, Saudi Arabia
;
2
Institute of Statistical Studies and Research, Cairo University, Faculty of Computing and Information Technology and King Abdulaziz University, Egypt
;
3
Faculty of Computer and Information Sciences and Ain Shams University, Saudi Arabia
Keyword(s):
DBSCAN Clustering Algorithm, Infrastructure City Planning, Spatial Clustering Algorithm, Urban Planning, Public Service Facility.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Expert Systems
;
Health Information Systems
;
Hybrid Intelligent Systems
;
Industrial Applications of AI
;
Knowledge Engineering and Ontology Development
;
Knowledge-Based Systems
;
Soft Computing
;
Symbolic Systems
Abstract:
This article present new algorithm for clustering data in the presence of obstacles. In real world, there exist many physical obstacles such as rivers, lakes, highways and mountains..., and their presence may affect the result of clustering significantly. In this paper, we study the problem of clustering in the presence of obstacles to solve location of public service facilities. Each facility must serve minimum pre-specified level of demand. The objective is to minimize the distance travelled by users to reach the facilities this means also to maximize the accessibility to facilities. To achieve this objective we developed CKB-WSP algorithm (Clustering using Knowledge-Based Systems and Weighted Short Path). This algorithm is Density-based clustering algorithm using Dijkstra algorithm to calculate obstructed short path distance where the clustering distance represents a weighted shortest path. The weights are associated with intersection node and represent the population number. Each
type of social facility(schools, fire stations, hospitals, mosque, church…) own many constraints such as surface area and number of people to be served, maximum distance, available location to locate these services. All these constraints is stored in the Knowledge-Based system. Comparisons with other clustering methods are presented showing the advantages of the CKB-WSP algorithm introduced in this paper.
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