Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Published in:
Adaptive Access Path Selection for Hardware-Accelerated DRAM Loads Read chapter Read first chapter

2018 | OriginalPaper | Chapter

DistClusTree: A Framework for Distributed Stream Clustering

Authors : Zhinoos Razavi Hesabi, Timos Sellis, Kewen Liao

Published in: Databases Theory and Applications

Publisher: Springer International Publishing

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

In this paper, we investigate the problem of clustering distributed multidimensional data streams. We devise a distributed clustering framework DistClusTree that extends the centralized ClusTree approach. The main difficulty in distributed clustering is balancing communication cost and clustering quality. We tackle this in DistClusTree through combining spatial index summaries and online tracking for efficient local and global incremental clustering. We demonstrate through extensive experiments the efficacy of the framework in terms of communication cost and approximate clustering quality.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
previous chapter Auto-CES: An Automatic Pruning Method Through Clustering Ensemble Selection
next chapter Mobile Application Based Heavy Vehicle Fatigue Compliance in Australian Operations
Literature
1.
Bifet, A., Holmes, G., Kirkby, R., Pfahringer, B.: MOA: massive online analysis. J. Mach. Learn. Res. 11, 1601–1604 (2010)
2.
Cormode, G., Muthukrishnan, S., Zhuang, W.: Conquering the divide: continuous clustering of distributed data streams. In: 2007 IEEE 23rd International Conference on Data Engineering, pp. 1036–1045, April 2007
3.
Ester, M., Kriegel, H.-P., Sander, J., Xu, X.: A density-based algorithm for discovering clusters a density-based algorithm for discovering clusters in large spatial databases with noise. In: Proceedings of the Second International Conference on Knowledge Discovery and Data Mining, KDD 1996, pp. 226–231. AAAI Press (1996)
4.
Guttman, A.: R-trees: a dynamic index structure for spatial searching, vol. 14. ACM (1984)CrossRef
5.
Januzaj, E., Kriegel, H.-P., Pfeifle, M.: Towards effective and efficient distributed clustering. In: Workshop on Clustering Large Data Sets ICDM, pp. 49–58 (2003)
6.
Kargupta, H., Huang, W., Sivakumar, K., Johnson, E.: Distributed clustering using collective principal component analysis. Knowl. Inf. Syst. 3, 2001 (1999)MATH
7.
Klusch, M., Lodi, S., Moro, G.: Distributed clustering based on sampling local density estimates. In: Proceedings of the 18th International Joint Conference on Artificial Intelligence, IJCAI 2003, pp. 485–490. Morgan Kaufmann Publishers Inc., San Francisco (2003)
8.
Kranen, P., Assent, I., Baldauf, C., Seidl, T.: The clustree: indexing micro-clusters for anytime stream mining. Knowl. Inf. Syst. 29(2), 249–272 (2011)CrossRef
9.
Rodrigues, P.P., Gama, J.: Distributed clustering of ubiquitous data streams. Wiley Interdisc. Rev. Data Mining Knowl. Disc. 4(01), 38–54 (2014)CrossRef
10.
White, D.A., Jain, R.: Similarity indexing with the SS-tree. In: Proceedings of the Twelfth International Conference on Data Engineering, pp. 516–523, February 1996
11.
12.
Zhou, A., Cao, F., Yan, Y., Sha, C., He, X.: Distributed data stream clustering: a fast EM-based approach. In: 2007 IEEE 23rd International Conference on Data Engineering, pp. 736–745, April 2007
Metadata
Title
DistClusTree: A Framework for Distributed Stream Clustering
Authors
Zhinoos Razavi Hesabi
Timos Sellis
Kewen Liao
Copyright Year
2018
Book
Databases Theory and Applications

Print ISBN: 978-3-319-92012-2

Electronic ISBN: 978-3-319-92013-9

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-319-92013-9_23

Premium Partner

    Image Credits