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Optimized Spatial Clustering for Target Finding and Maximum Effects
Simon Fong1; Suash Deb2
2014-10-08
Conference Name2013 International Conference on Machine Intelligence Research and Advancement
Source PublicationProceedings - 2013 International Conference on Machine Intelligence Research and Advancement, ICMIRA 2013
Pages247-252
Conference Date21-23 Dec. 2013
Conference PlaceKatra, India
PublisherIEEE, 345 E 47TH ST, NEW YORK, NY 10017 USA
Abstract

Spatial clustering is the process of grouping a set of spatial objects so that objects within the same group have high similarity. In the context of GIS, similar objects in a cluster share certain common characteristics such as proximity and 'importance' in relation to some purpose. We consider a special case of 'spatial groups' pertaining to a common purpose in this paper. Given the data that have different densities distributed over a geographical area, how unique groups could be formed over them in order to maximize the total coverage by these groups. By maximizing the coverage, applications could be either destructive or constructive by intension, e.g. a jet fighter pilot needs to make a real-time critical decision at a split of second to locate several separate targets to hit in order to cause maximum damage, when it flies over an enemy terrain, a town planner is considering where to station certain resources (sites for schools and hospitals, security patrol routes, air-born food ration drops for humanitarian aid, etc.) for maximum effect, given a vast area of different distribution of densities for benevolent purposes. An optimized grouping algorithm developed by the authors in linear programming is compared with classical K-means, over two different case studies in this paper.

KeywordClustering Linear Programming Spatial Grouping Target Finding
DOIhttps://doi.org/10.1109/ICMIRA.2013.53
URLView the original
Indexed BySCI
Language英语
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic
WOS IDWOS:000353945100041
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Cited Times [WOS]:0   [WOS Record]     [Related Records in WOS]
Document TypeConference paper
CollectionDEPARTMENT OF COMPUTER AND INFORMATION SCIENCE
Affiliation1.Department of Computer and Information Science, University of Macau, Taipa, Macau SAR
2.Department of Computer Science and Engineering Cambridge Institute of Technology, Ranchi, India
First Author AffilicationUniversity of Macau
Recommended Citation
GB/T 7714
Simon Fong,Suash Deb. Optimized Spatial Clustering for Target Finding and Maximum Effects[C]:IEEE, 345 E 47TH ST, NEW YORK, NY 10017 USA,2014:247-252.
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