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:
Recognition of Fuzzy or Incompletely Described Objects Read chapter Read first chapter

2018 | OriginalPaper | Chapter

Distributed DBSCAN Algorithm – Concept and Experimental Evaluation

Authors : Adam Merk, Piotr Cal, Michał Woźniak

Published in: Proceedings of the 10th International Conference on Computer Recognition Systems CORES 2017

Publisher: Springer International Publishing

Log in

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

search-config
loading …

Abstract

One of the most popular clustering algorithm is DBSCAN, which is known to be efficient and highly resistant to noise. In this paper we propose its distributed implementation. Distributed computing is a very fast growing way of solving problems in big datasets using a multinode cluster, rather than parallelization in one computer. Using its features in proper way, can lead to higher performance and, what is probably more important, higher scalability. In order to show added value of this way of designing and implementing algorithms we compare our results with GPU parallelization. On the basis of the obtained results We formulate the propositions how to improve our solution.

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 Real-Time Image Content Assessment for Underwater Robot Manoeuvring Based on Structural Tensor Analysis
next chapter Combining Active Learning and Self-Labeling for Data Stream Mining
Literature
1.
Cal, P., Woźniak, M.: Data preprocessing with GPU for DBSCAN algorithm. In: Proceedings of the 8th International Conference on Computer Recognition Systems, CORES 2013, pp. 793–801 (2013)
2.
Ester, M., Kriegel, H.-P., Sander, J., Xu, X.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: Simoudis, E., Han, J., Fayyad, U.M. (eds.) Proceedings of the Second International Conference on Knowledge Discovery and Data Mining (KDD 1996), pp. 226–231. AAAI Press (1996)
3.
Wang, R.Y., Storey, V.C., Firth, C.P.: A framework for analysis of data quality research. IEEE Trans. Knowl. Data Eng. 7, 623–640 (1995)CrossRef
4.
Zhu, X., Wu, X.: Class noise vs attribute noise: a quantitative study. Artif. Intell. Rev. 22, 177–210 (2004)CrossRefMATH
5.
Wu, X., Zhu, X.: Mining with noise knowledge: error-aware data mining. IEEE Trans. Syst. Man Cybern. 38, 917–932 (2008)CrossRef
6.
Brecheisen, S., Kriegel, H.-P., Pfeifle, M.: Parallel density-based clustering of complex objects. In: PAKDD 2006, pp. 179–188. Springer, Heidelberg (2006)
7.
Li, H., Chen, M., Gao, X.: Parallel dbscan with priority r-tree. In: Information Management and Engineering (ICIME) (2010)
8.
Patwary, M.A., Palsetia, D., Agrawal, A., Liao, W.-K., Manne, F., Choudhary, A.: A new scalable parallel dbscan algorithm using the disjoint-set data structure. In: Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis, SC 2012, pp. 62:1–62:11. IEEE Computer Society Press, Los Alamitos (2012)
9.
Xu, X., Jäger, J., Kriegel, H.-P.: A fast parallel clustering algorithm for large spatial databases. Data Min. Knowl. Discov., 263–290 (1999)
10.
Dean, J., Ghemawat, S.: Mapreduce: simplified data processing on large clusters. In: OSDI 2004, pp. 137–150 (2004)
11.
White, T.: Hadoop, The Definitive Guide. O’Reilly Media Inc. (2012)
12.
13.
Karau, H., Konwinski, A., Wendell, P., Zaharia, M.: Learning Spark: Lightning-Fast Big Data Analytics. O’Reilly Media, Incorporated (2015)
14.
15.
Baldi, P., Sadowski, P., Whiteson, D.: Searching for exotic particles in high-energy physics with deep learning. Nat. Commun. 5, 2 July 2014
16.
Porwik, P., Doroz, R.: Self-adaptive biometric classifier working on the reduced dataset. In: 9th International Conference on Hybrid Artificial Intelligence Systems (HAIS), Salamanca. Spain Book Series, LNCS, vol. 8480, pp. 377–388 (2014)
Metadata
Title
Distributed DBSCAN Algorithm – Concept and Experimental Evaluation
Authors
Adam Merk
Piotr Cal
Michał Woźniak
Copyright Year
2018
Book
Proceedings of the 10th International Conference on Computer Recognition Systems CORES 2017

Print ISBN: 978-3-319-59161-2

Electronic ISBN: 978-3-319-59162-9

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-319-59162-9_49

Premium Partner

    Image Credits