An auto-stopped hierarchical clustering algorithm integrating outlier detection algorithm

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 3739 LNCS, Issue , 2005, Pages 464-474

  Lv, Tian Yang ^a,b   Su, Tai Xue ^a   Wang, Zheng Xuan ^a   Zuo, Wan Li ^a  

^a JILIN UNIVERSITY   (China)

^b HARBIN ENGINEERING UNIVERSITY   (China)

Author keywords

Auto Stop; Clustering; Outlier Mining

Indexed keywords

ARTIFICIAL INTELLIGENCE; DATA MINING; DATABASE SYSTEMS; INFORMATION MANAGEMENT; INTELLIGENT AGENTS; PROBLEM SOLVING;

AUTO STOP; CLUSTERING; OUTLIER MINING;

ALGORITHMS;

EID: 33646528181     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11563952_41     Document Type: Conference Paper

References (15)

1. C. Rosenberger, K. Chehdi: Unsupervised Clustering Method with Optimal Estimation of the Number of Clusters: Application to Image Segmentation. International Conference on Pattern Recognition, Volume 1 (Sep. 2000) 656-659
2. Xuejian Xiong. Kap Luk Chan. Towards: An Unsupervised Optimal Fuzzy Clustering algorithm for Image Database Organization. International Conference on Pattern Recognition, Volume 3. (Sep. 2000) 3909-3913
3. Johannes Gehrke: Report on the SIGKDD 2001 Conference Panel "New Research directions in KDD", SIGKDD Explorations, 3(2), (2002) 76-77
4. S. Guha, R. Rastogi, K. Shim: CURE: an Efficient Clustering Algorithm for Large Database. In: Laura M. Haas and Ashutosh Tiwary, eds. Proceedings of the ACM SIGMOD Conference on Management of Data. Seattle, Washington: ACM Press (1998) 73-84
5. S. Guha, R. Rastogi, K. Shim: ROCK: a robust clustering algorithm for categorical attributes. In: Proc. of the 15th Int'l Conf. on Data Eng (1999) 512-521
6. Tian Zhang, etc: BIRCH: An Efficient Data Clustering Method for Very Large Databases. In: Proceedings of the 1996 ACM SIGMOD International Conference on Management of Data (1996) 103-114
7. M. Ester, H.-P. Kriegel, J. Sander, and X. Xu: A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise. In: Proceedings of 2nd International Conference on Knowledge Discovery and Data Mining, Portland, OR (1996) 226-231
8. Ana L.N. Fred, José M.N. Leitão: A new Cluster Isolation criterion Based on Dissimilarity Increments. IEEE Transactions on Pattern Analysis and Machine Intelligence, VOL. 25, No. 8 (August 2003) 944-958
9. http://www.statdaddy.com/
10. Edwin M. Knorr, Raymond T. Ng: Finding Intensional Knowledge of Distance-Based outliers. In: Proceedings of the 25th Very Large Data Bases conference. Edinburgh, Scotland (1999) 211-222
11. Sridhar Ramaswamy, Rajeev Rastogi, Kyuseok Shim. Efficient Algorithms for mining outliers from Large Data Sets. Proceedings of the 2000 ACM SIGMOD international conference on Management of data. Dallas, Texas, United States (2000) 427-438
12. Hettich, S. & Blake, C.L. & Merz, C.J. (1998). UCI Repository of machine learning databases [http://www.ics.uci.edu/~mlearn/MLRepository.html]. Irvine, CA: University of California, Department of Information and Computer Science.
13. Ying Zhao, George Karypis: Criterion Functions for Document Clustering: Experiment and Analysis. Technical Report #01-40, University of Minnesota (2001) 1-40
14. C. Faloutsos, K. Lin: FastMap: A Fast Algorithm for Indexing, Data-Mining and Visualization of Traditional and Multimedia Datasets. In: Proceedings of 1995 ACM SIGMOD, SIGMOD RECORD, vol.24, no.2 (June 1995) 163-174
15. Hawkins D: Identification of Outliers. Chapman and Hall, London. (1980)