Distributed clustering algorithm based on ensemble learning

Dongnan Daxue Xuebao (Ziran Kexue Ban)/Journal of Southeast University (Natural Science Edition)

Volumn 37, Issue 4, 2007, Pages 585-588

  Ji, Genlin ^a   Ling, Xiaohan ^a   Yang, Ming ^a  

^a NANJING NORMAL UNIVERSITY   (China)

Author keywords

Data mining; Distributed clustering; Ensemble learning; K means

Indexed keywords

COMBINATORIAL OPTIMIZATION; DATA MINING; LEARNING SYSTEMS; MATHEMATICAL MODELS; SERVERS; VERIFICATION;

DISTRIBUTED CLUSTERING; DISTRIBUTED K MEANS; ENSEMBLE LEARNING; GLOBAL SITES; LOCAL SITES;

CLUSTERING ALGORITHMS;

EID: 34547976381     PISSN: 10010505     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (10)

1. Mclachlan G, Basford K. Mixture models: inference and application to clustering [M]. New York: Dekker Press, 1988.
2. Ester M, Kriegel H P, Sander J, et al. A density based algorithm for discovering clusters in large spatial databases with noise [C]//Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining. Portland, CA: AAAI Press, 1996: 226-231.
3. Xu X, Jager J, Kriegel H P. A fast parallel clustering algorithm for large spatial databases [J]. Data Mining and Knowledge Discovery, 1999, 3(3): 263-290.
4. Ni Weiwei, Lu Jieping, Sun Zhihui. An effective distributed K-means clustering algorithm based on the pre-treatment of vector's inner-product [J]. Journal of Computer Research and Development, 2005, 42(9): 1493-1497. (in Chinese)
5. Kriegel H P, Kroger P, Pryakhin A, et al. Effective and efficient distributed model-based clustering [C]//Proceedings of the 5th IEEE International Conference on Data Mining. Houston, TX, USA, 2005: 258-265.
6. Prodip H, Lawrence O H. Scalable clustering: a distributed approach [C]//IEEE International Conference on Fuzzy Systems. Budapest, Hungary, 2004: 143-148.
7. Dietterich T G. Machine learning research: four current directions [J]. AI Magazine, 1997, 18(4): 97-136.
8. Strehl A, Ghosh J. Cluster ensembles-a knowledge reuse framework for combining partitionings [C]//Proceedings of the 18th National Conference on Artificial Intelligence. Menlo Park: AAAI Press, 2002: 93-98.
9. Newman D J, Hettich S, Blake C L, et al. UCI repository of machine learning databases [EB/OL]. (1998) [2006-6-18]. http://www.ics.uci.edu/-mlearn/MLRepository.html.
10. Modha D S, Spangler W S. Feature weighting in K-means clustering [J]. Machine Learning, 2003, 52(3): 217-237.