Genetic K-Medoids Spatial Clustering with Obstacles Constraints

Spatial clustering is an important research topic in spatial data mining (SDM). It is not only an important effective method but also a prelude of other task for SDM. Grouping similar data in large 2-dimensional spaces to find hidden patterns or meaningful sub-groups has many applications such as sa...

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Vydáno v:2006 3rd International IEEE Conference Intelligent Systems s. 826 - 831
Hlavní autoři: Xueping Zhang, Jiayao Wang, Fang Wu, Zhongshan Fan, Wenbo Xu
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 01.09.2006
Témata:
Application software
Biomedical imaging
Clustering
Clustering algorithms
Clustering methods
Data mining
Genetic algorithms
Genetic engineering
Geographic Information Systems
K-Medoids Algorithm
Laboratories
Obstacles Constraints
Partitioning algorithms
ISBN:9781424401956, 142440195X
ISSN:1541-1672
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Shrnutí:Spatial clustering is an important research topic in spatial data mining (SDM). It is not only an important effective method but also a prelude of other task for SDM. Grouping similar data in large 2-dimensional spaces to find hidden patterns or meaningful sub-groups has many applications such as satellite imagery, geographic information systems, medical image analysis, marketing, computer visions, etc. So, many methods have been proposed in the literature, but few of them have taken into account constraints that may be present in the data or constraints on the clustering. These constraints have significant influence on the results of the clustering process of large spatial data. In this paper, we discuss the problem of spatial clustering with obstacles constraints and propose a novel spatial clustering method based on genetic algorithms (GAs) and K-Medoids, called GKSCOC, which aims to cluster spatial data with obstacles constraints. It can not only give attention to higher local constringency speed and stronger global optimum search, but also consider the obstacles constraints and make the results of spatial clustering more practice. Its performance has compared to GAs, K-Medoids; and the results on real datasets show that it is better than standard GAs and K-Medoids. The drawback of this method is a comparatively slower speed in spatial clustering
ISBN:9781424401956
142440195X
ISSN:1541-1672
DOI:10.1109/IS.2006.348527