Data stream summarization by histograms clustering

Studies in Classification, Data Analysis, and Knowledge Organization

Volumn , Issue , 2013, Pages 27-35

  Balzanella, Antonio ^a   Rivoli, Lidia ^b   Verde, Rosanna ^a  

^a SECOND UNIVERSITY OF NAPLES   (Italy)

^b UNIVERSITY OF NAPLES FEDERICO II   (Italy)

Author keywords

[No Author keywords available]

Indexed keywords

CLUSTERING ALGORITHMS; DATA MINING; GRAPHIC METHODS;

DATA STREAM MINING; EVOLVING DATUM; FREQUENCY DISTRIBUTIONS; GRAPHICAL TOOLS; KNOWLEDGE DISCOVERY PROCESS; NONSTATIONARY PROCESS;

DATA STREAMS;

EID: 84892581988     PISSN: 14318814     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1007/978-3-319-00032-9_4     Document Type: Conference Paper

References (13)

1. Aggarwal, C. C., Han, J., Wang, J., & Philip, S. (2003). A framework for clustering evolving data streams. In: 29th int. conf. on very large data bases.
2. Babcock, B., Babu, S., Datar, M., Motwani, R., & Widom, J. (2002). Models and issues in data stream systems. In: 21th ACM SIGMOD-SIGACT-SIGART symposium PODS '02 (pp. 1-16).
3. Balzanella, A. (2009). Clustering and summarizing massive data streams. PHD Thesis, http://www.fedoa.unina.it/4184(2009.
4. Balzanella, A., Irpino, A., & Verde, R. (2010). Dimensionality reduction techniques for streaming time series: A new symbolic approach. Studies in classification, data analysis, and knowledge organization (pp. 381-389). Heidelberg, Berlin: Springer.
5. Balzanella, A., Romano, E., & Verde, R. (2011). Summarizing and mining streaming data via a functional data approach. Classification and multivariate analysis for complex data structures (pp. 409-416). Heidelberg, Berlin: Springer.
6. Gama, J., & Gaber, M. M. (2007). Learning from data stream. Techniques in sensor networks. Heidelberg, Berlin: Springer.
7. Guha, S., Koudas, N., & Shim, K. (2001). Data-streams and histograms. In: 33th annual ACM symposium on theory of computing (pp. 471-475). New York: ACM.
8. Irpino, A., & Verde, R. (2006). Dynamic clustering of histograms using Wasserstein metric. In A. Rizzi, & M. Vichi (Eds.) COMPSTAT 2006 - Advances in computational statistics (pp. 869- 876). Heidelberg: Physica-Verlag.
9. Mallows, C. L. (1972). A note on asymptotic joint normality. Annals of Mathematical Statistics, 43(2), 508-515.
10. Sebastiao, R., & Gama, J. (2007). Change detection in learning histograms from data streams. Progress in Artificial Intelligence. Lecture Notes in Computer Science. Springer Berlin Heidelberg. ISBN: 978-3-540-77000-8.
11. Verde, R., & Irpino, A. (2007). Dynamic clustering of histogram data: using the right metric. Studies in Classification, Data Analysis, and Knowledge Organization, Part I, 123-134, doi: 10.1007/978-3-540-73560-1 12.
12. Verde, R., & Irpino, A. (2010). Ordinary least squares for histogram data based on wasserstein distance. In Y. Lechevallier, & G. Saporta (Eds.) COMPSTAT 2010 (pp. 581588). Berlin: PhysicaVerlag.
13. Vitter, J. S. (1985). Random sampling with a reservoir. ACM Transactions on Mathematical Software, 11(1), 37-57.