A Clustering Algorithm for Network Objects with Direction Factors
TANG Liang1,2,3, FANG Ting-Jian1,3
1.Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei 230031 2.Department of Automation, University of Science and Technology of China, Hefei 230026 3.Research Center for ITS Engineering Technology of Anhui Province, Hefei 230088
Abstract Clustering methods are analyzed in which Euclidean distance and network distance are used as a similarity measure respectively. The neighbor correlation between objects on a spatial network is discussed and a clustering algorithm is proposed for network objects with consideration of direction factors. The algorithm combines the two distances as the similarity measure of clustering by using the neighbor correlation. The analysis and experimental results indicate that the effectiveness of the proposed algorithm is better than those only using one measure.
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2009, Vol. 22 
