2[2]Jianwei Han, M Kamber. Data Mining: Concepts and Techniques. San Francisco: Morgan Kaufmann Publishers, 2000
3[3]J Grabmeier, A Rudolph. Techniques of cluster algorithms in data mining. Data Mining and Knowledge Discovery, 2002, 6(4): 303~360
4[4]A K Jain, M N Murty, P J Flynn. Data clustering: A review. ACM Computing Surveys, 1999, 31(3): 264~323
5[5]J MacQueen. Some methods for classification and analysis of multivariate observations. In: L M Le Cam, J Neyman eds. Proc of the 5th Berkeley Symp on Mathematics, Statics and Probability, Vol 1. Berkeley: Berkeley University of California Press, 1967. 281~298
6[6]J C Bezdek. Pattern Recognition with Fuzzy Objective Function Algorithms. New York: Plenum Press, 1981
7[7]L Kaufman, P J Rousseeuw. Finding Groups in Data: An Introduction to Cluster Analysis. New York: John Wiley & Sons, 1990
8[8]M Ester, H P Kriegel, J Sander et al. A density-based algorithm for discovering clusters in large spatial databases with noise. In: E Simoudis, J Han, U Fayyad eds. Proc of the 2nd Int'l Conf on Knowledge Discovery and Data Mining (KDD-96). Menlo Park: AAAI Press, 1996, 226~231
9[9]M Ankerst, M Breuning, H P Kriegel et al. OPTICS: Ordering points to identify the clustering structure. In: A Delis, C Faloutsos, S Ghandeharizadeh eds. Proc of the 1999 ACM SIGMOD Int'l Conf on Management of Data. New York: ACM Press, 1999. 49~60
10[10]W Wang, J Wang, R Muntz. STING: A statistical information grid approach to spatial data mining. In: M Jarke, M J Carey, K R Dittrich et al eds. Proc of the 23rd IEEE Int'l Conf on Very Large Data Bases. San Francisco: Morgan Kaufmann Publishers, 1997, 186~195