A new online clustering approach for data in arbitrary shaped clusters

Proceedings - 2015 IEEE 2nd International Conference on Cybernetics, CYBCONF 2015

Volumn , Issue , 2015, Pages 228-233

  Hyde, Richard ^a   Angelov, Plamen ^a  

^a LANCASTER UNIVERSITY   (United Kingdom)

Author keywords

arbitrary shape clusters; big data; clustering; data streams; online clustering

Indexed keywords

BIG DATA; COMPUTATIONAL EFFICIENCY; EFFICIENCY;

ARBITRARY SHAPE; CLUSTERING; DENSITY-BASED CLUSTERING; JOINING TECHNIQUES; MEMORY EFFICIENCY; ONLINE-CLUSTERING; THRESHOLD-VALUE; TWO-STAGE PROCESS;

DATA STREAMS;

EID: 84947967804     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CYBConf.2015.7175937     Document Type: Conference Paper

References (6)

1. R. Dutta Baruah and P. Angelov, "Evolving local means method for clustering of streaming data," IEEE International Conference on Fuzzy Systems, pp. 10-15, 2012.
2. R. D. Baruah and P. Angelov, "DEC: Dynamically evolving clustering and its application to structure identification of evolving fuzzy model," Transaction on Cybernetics, pp. 1-16, 2013.
3. V. Chaoji, M. Al Hasan, S. Salem, and M. J. Zaki, "SPARCL: Efficient and effective shape-based clustering," Proceedings-IEEE International Conference on Data Mining, ICDM, pp. 93-102, 2008.
4. G. Karypis, E.-H. Han, and V. Kumar, "Chameleon: hierarchical clustering using dynamic modeling," Computer, vol. 32, pp. 68-75, 1999.
5. F. Cao, M. Ester, W. Qian, and A. Zhou, "Density-based clustering over an evolving data stream with noise," . . . Conference on Data Mining, no. 2, pp. 328-339, 2006.
6. J. B. MacQueen, "Kmeans Some Methods for classification and Analysis of Multivariate Observations," 5th Berkeley Symposium on Mathematical Statistics and Probability 1967, vol. 1, no. 233, pp. 281-297, 1967. [Online]. Available: http://projecteuclid. org/euclid. bsmsp/1200512992