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http://yadda.icm.edu.pl:443/baztech/element/bwmeta1.element.baztech-95117230-511c-45d0-b1ae-83d4cf2926d8

Czasopismo

Measurement Automation Monitoring

Tytuł artykułu

Spatial data clustering in independent mobile environment

Autorzy Gajewski, B.  Martyn, T. 
Treść / Zawartość
Warianty tytułu
Języki publikacji EN
Abstrakty
EN Most geolocation applications for mobile devices assume a constant connection with the network and high computational power nodes. However, with ever-developing devices it now becomes possible to establish peer-to-peer networks in case when the network can be unreachable due to special circumstances (like conflicts or natural disasters). In this paper, a method for clustering spatial data in mobile environment is discussed. A simple solution based on OPTICS algorithm with lexical distance is proposed for grouping the observations.
Słowa kluczowe
EN peer-to-peer   data clustering   OPTICS   mobile   lexical distance  
Wydawca Wydawnictwo PAK
Czasopismo Measurement Automation Monitoring
Rocznik 2016
Tom Vol. 62, No. 5
Strony 163--165
Opis fizyczny Bibliogr. 13 poz., rys., tab., wzory
Twórcy
autor Gajewski, B.
  • Warsaw University of Technology, Institute of Computer Science 15/19 Nowowiejska St., 00-665 Warsaw, Poland, b.gajewski@ii.pw.edu.pl
autor Martyn, T.
  • Warsaw University of Technology, Institute of Computer Science 15/19 Nowowiejska St., 00-665 Warsaw, Poland, martyn@ii.pw.edu.pl
Bibliografia
[1] Pence Harry E.: Smartphones, smart objects, and augmented reality. The Reference Librarian 52.1-2 (2010), pp. 136-145.
[2] Ester Martin, et al.: A density-based algorithm for discovering clusters in large spatial databases with noise. Kdd. Vol. 96, No. 34, 1996.
[3] Wang Wei, Jiong Yang, and Muntz Richard: STING: A statistical information grid approach to spatial data mining. VLDB, Vol. 97, 1997.
[4] Ankerst Mihael, et al.: OPTICS: ordering points to identify the clustering structure. ACM Sigmod Record. Vol. 28, No. 2, ACM, 1999.
[5] Altman N. S.: An introduction to kernel and nearest-neighbor nonparametric regression. The American Statistician 46 (3): 175–185, 1992.
[6] Koperski K., and Jiawei Han: Data mining methods for the analysis of large geographic databases. Proc. of 10th Annual Conf. on GIS, Vancouver, BC, 1996.
[7] Cichosz P.: Data Mining Algorithms: Explained Using R. John Wiley & Sons, 2014.
[8] Chmielewski M., Gałka A.: Automated mapping JC3IEDM data in tactical symbology standards for Common Operational Picture services. Proceedings of the Military Communications and Information Systems Conference MCC. 2009.
[9] Levenshtein V.I.: Binary codes capable of correcting deletions, insertions, and reversals." Soviet physics doklady, Vol. 10, No. 8, 1966.
[10] Navarro G.: A guided tour to approximate string matching. ACM computing surveys (CSUR) 33.1 (2001): 31-88.
[11] mCOP application homepage at http://uranus.wat.edu.pl:8808/ wiki/index.php/MCOP
[12] Gajewski B. K., and Martyn T.: Smart mobile P2P communication optimization for close range by an automatic interface switch. Measurement Automation and Monitoring, vol. 61, no. 07, 2015, pp. 317–319.
[13] ELKI: Environment for Developing KDD-Applications Supported by Index-Structures Open Source project. http://elki.dbs.ifi.lmu.de/
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