Spatial clustering algorithm with obstacles constraints by quantum particle swarm optimization and K-Medoids

The classical K-Medoids algorithm is easily trapped into local extremum and is sensitive to initialization. After analyzed the existing algorithms of spatial clustering with obstacles constraints, the paper proposed a new spatial clustering algorithm with obstacles constraints combined QPSO with K-M...

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Bibliographic Details
Published in:2010 Second International Conference on Computational Intelligence and Natural Computing Vol. 2; pp. 105 - 108
Main Authors: Yang Teng-Fei, Zhang Xue-Ping
Format: Conference Proceeding
Language:English
Published: IEEE 01.09.2010
Subjects:
Algorithm design and analysis
Clustering algorithms
Convergence
K-Medoids algorithm
obstacle constraints
Optimization
Particle swarm optimization
Partitioning algorithms
QPSO algorithm
spatial clustering
Sun
ISBN:9781424477050, 1424477050
Online Access:Get full text
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Summary:The classical K-Medoids algorithm is easily trapped into local extremum and is sensitive to initialization. After analyzed the existing algorithms of spatial clustering with obstacles constraints, the paper proposed a new spatial clustering algorithm with obstacles constraints combined QPSO with K-Medoids, which named QKSCO. This algorithm introduced QPSO's rapid global convergence to separating the global clusters firstly, then it finds the optimal exact solutions of clusters by K-Medoids; and it called the two algorithms to improving the efficiency of the implementation of the new algorithm coordinating. The experimental results indicated that the algorithm has better time complexity and clustering efficiency.
ISBN:9781424477050
1424477050
DOI:10.1109/CINC.2010.5643776