Unsupervised rough clustering method based on Gaussian mixture model

Harbin Gongye Daxue Xuebao/Journal of Harbin Institute of Technology

Volumn 38, Issue 2, 2006, Pages

  He, Ming ^a   Feng, Bo Qin ^a   Ma, Zhao Feng ^a   Fu, Xiang Hua ^a  

^a XI'AN JIAOTONG UNIVERSITY   (China)

Author keywords

Clustering; EM algorithm; Gaussian mixture model; Rough set

Indexed keywords

ALGORITHMS; CALCULATIONS; ITERATIVE METHODS; MATHEMATICAL MODELS; MAXIMUM LIKELIHOOD ESTIMATION; PROBABILITY DENSITY FUNCTION; PROBABILITY DISTRIBUTIONS; ROUGH SET THEORY;

EXPECTATION MAXIMIZATION (EM) ALGORITHM; GAUSSIAN MIXTURE MODEL; K MEANS CLUSTERING ALGORITHM; ROUGH SET;

DATA MINING;

EID: 33646748357     PISSN: 03676234     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (15)

1. JAIN A K, MURTY M N, FLYNN P J. Data clustering: Review [J]. ACM Computing Surveys (CSUR), 1999, 31(3): 264-323.
2. HAN J W, KAMBER M. Data Mining: Concepts and Techniques [M]. San Francisco: Morgan Kaufmann, 2001.
3. ZHANG T, RAMAKRISHNAN R, LIVNY M. BIRCH: An efficient data clustering method for very large databases [A]. Proceedings of the 1996 ACM SIGMOD International Conference on Management of Data [C]. New York: ACM Press, 1996. 103-114.
4. GUHA S, RASTOGI R, SHIM K. CURE: An efficient data clustering method for very large databases [J]. ACM SIGMOD Record, 1998, 27(2): 73-84.
5. KARYPIS G, HAN E H, KUMARL V. CHAMELEON: A hierarchical clustering algorithm using dynamic modeling [J]. Computer, 1999, 32(8): 68-75.
6. ESTER M, KRIEGEL H P, SANDER J, et al. A density2based algorithm for discovering clusters in large spatial databases [A]. Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining (KDD'96) [C]. Portland, OR: AAAI Press, 1996. 226-231.
7. ANKERST M, BREUNING M M, KRIEGEL H P, et al. OPTICS: Ordering points to identify the clustering structure [A]. Proceedings of the 1999 ACM SIGMOD International Conference on Management of Data [C]. Philadelphia, Pennsylvania: ACM Press, 1999. 49-60.
8. AGRAWAL R, GEHRKE J, GUNOPULOS D, et al. Automatic subspace clustering of high dimensional data for data mining applications [A]. Proceedings of the 1996 ACM SIGMOD International Conference on Management of Data [C]. Data Seattle, Washington: ACM Press, 1998. 94-105.
9. PAWLAK Z. Rough sets and intelligent data analysis [J]. Information Science, 2002, 147(11): 1-12.
10. LAURITZEN S L. The EM algorithm for graphical association models with missing data [J]. Computational Statistics and Data Analysis, 1995, 19: 191-201.
11. PAWALK Z. Rough sets [J]. International Journal of Computer and Information Science, 1982, 11(5): 341-356.
12. PAWALK Z. Theoretical Aspects of Reasoning about Data [M]. Boston: Kluwer Academic Publishers, 1991.
13. GUO Y W, QING L, YI Y Y, et al. Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing [M]. Berlin: Springer-Verlag, 2003.
14. PAWLAK Z. Theorize with data using rough sets [J]. Computer Software and Application Conference, 2002, 8: 26-29.
15. HIRANO S, TSUMOTO S, INUIGUCHI M, et al. Rough Sets Theory and Granular Computing [M]. Berlin: Springer-Verlag, 2001.