Research on k-means clustering algorithm: An improved k-means clustering algorithm

3rd International Symposium on Intelligent Information Technology and Security Informatics, IITSI 2010

Volumn , Issue , 2010, Pages 63-67

  Shi, Na ^a   Liu, Xumin ^a   Guan, Yong ^a  

^a CAPITAL NORMAL UNIVERSITY   (China)

Author keywords

Clustering analysis; Computational complexity; Distance; k means algorithm

Indexed keywords

ANALYTICAL METHOD; CLUSTER CENTERS; CLUSTERING ANALYSIS; CLUSTERING RESULTS; DATA OBJECTS; IMPROVED K-MEANS ALGORITHM; IMPROVED METHODS; K-MEANS; K-MEANS ALGORITHM; K-MEANS CLUSTERING ALGORITHM; RUNNING TIME;

CLUSTER ANALYSIS; CLUSTER COMPUTING; COMPUTATIONAL COMPLEXITY; COMPUTATIONAL METHODS; DATA STRUCTURES; INFORMATION SCIENCE; INFORMATION TECHNOLOGY; WATER SUPPLY SYSTEMS;

CLUSTERING ALGORITHMS;

EID: 77952695482     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IITSI.2010.74     Document Type: Conference Paper

References (15)

1. Yuan F, Meng Z. H, Zhang H. X and Dong C. R, "A New Algorithm to Get the Initial Centroids," Proc. of the 3rd International Conference on Machine Learning and Cybernetics, pp. 26-29, August 2004.
2. Sun Jigui, Liu Jie, Zhao Lianyu, "Clustering algorithms Research",Journal of Software ,Vol 19,No 1, pp.48-61,January 2008.
3. Sun Shibao, Qin Keyun,"Research on Modified k-means Data Cluster Algorithm"I. S. Jacobs and C. P. Bean, "Fine particles, thin films and exchange anisotropy," Computer Engineering, vol.33, No.13, pp.200-201,July 2007.
4. Merz C and Murphy P, UCI Repository of Machine Learning Databases, Available: ftp://ftp.ics.uci.edu/pub/machine-learning-databases
5. Fahim A M,Salem A M,Torkey F A, "An efficient enhanced k-means clustering algorithm" Journal of Zhejiang University Science A, Vol.10, pp:1626-1633,July 2006.
6. Zhao YC, Song J. GDILC: A grid-based density isoline clustering algorithm. In: Zhong YX, Cui S, Yang Y, eds. Proc. of the Internet Conf. on Info-Net. Beijing: IEEE Press, 2001. 140-145. http://ieeexplore.ieee.org/iel5/ 7719/21161/00982709.pdf
7. Huang Z, "Extensions to the k-means algorithm for clustering large data sets with categorical values," Data Mining and Knowledge Discovery, Vol.2, pp:283-304, 1998. (Pubitemid 128695480)
8. K.A.Abdul Nazeer, M.P.Sebastian, "Improving the Accuracy and Efficiency of the k-means Clustering Algorithm",Proceeding of the World Congress on Engineering, vol 1,london, July 2009.
9. Fred ALN, Leitão JMN. Partitional vs hierarchical clustering using a minimum grammar complexity approach. In: Proc. of the SSPR & SPR 2000. LNCS 1876, 2000. 193-202. http://www.sigmod.org/dblp/db/conf/sspr/sspr2000.htm
10. Gelbard R, Spiegler I. Hempel's raven paradox: A positive approach to cluster analysis. Computers and Operations Research, 2000,27(4):305-320. (Pubitemid 30144757)
11. Huang Z. A fast clustering algorithm to cluster very large categorical data sets in data mining. In: Proc. of the SIGMOD Workshop on Research Issues on Data Mining and Knowledge Discovery. Tucson, 1997. 146-151. http://www.informatik.uni-trier.de/~ley/db/conf/sigmod/sigmod97.html
12. Ding C, He X. K-Nearest-Neighbor in data clustering: Incorporating local information into global optimization. In: Proc. of the ACM Symp. on Applied Computing. Nicosia: ACM Press, 2004. 584-589. http://www.acm.org/conferences/ sac/sac2004/
13. Hinneburg A,Keim D.An efficient approach to clustering in large multimedia databases with noise.In:Agrawal R,Stolorz PE,Piatetsky- Shapiro G,eds.Proc.of the 4th Int'l Conf.on Knowledge Discovery and Data Mining(KDD'98).New York:AAAIPress,1998.58-65
14. Zhang T,Ramakrishnan R,Livny M.BIRCH:An efficient data clustering method for very large databases.In:Jagadish HV,Mumick IS,eds.Proc.of the 1996 ACM SIGMOD Int'l Conf.on Management of Data.Montreal:ACM Press,1996.103-114
15. Birant D, Kut A. ST-DBSCAN: An algorithm for clustering spatialtemporal data. Data & Knowledge Engineering, 2007,60(1): 208-221. (Pubitemid 46053593)