• DocumentCode
    2785238
  • Title

    An Attempt to Find Neighbors

  • Author

    Shi, Yong ; Rosenblum, Ryan

  • Author_Institution
    Dept. of Comput. Sci. & Inf. Syst., Kennesaw State Univ., Kennesaw, GA, USA
  • fYear
    2010
  • fDate
    10-12 Oct. 2010
  • Firstpage
    318
  • Lastpage
    320
  • Abstract
    In this paper, we present our continuous research on similarity search problems. Previously we proposed PanKNN[18] which is a novel technique that explores the meaning of K nearest neighbors from a new perspective, redefines the distances between data points and a given query point Q, and efficiently and effectively selects data points which are closest to Q. It can be applied in various data mining fields. In this paper, we present our approach to solving the similarity search problem in the presence of obstacles. We apply the concept of obstacle points and process the similarity search problems in a different way. This approach can assist to improve the performance of existing data analysis approaches.
  • Keywords
    data analysis; search problems; K nearest neighbors; data analysis; obstacle points; similarity search problems; Algorithm design and analysis; Clustering algorithms; Data mining; Measurement; Nearest neighbor searches; Search problems; USA Councils; Fuzzy; K nearest neighbors; data mining; query; similarity;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC), 2010 International Conference on
  • Conference_Location
    Huangshan
  • Print_ISBN
    978-1-4244-8434-8
  • Electronic_ISBN
    978-0-7695-4235-5
  • Type

    conf

  • DOI
    10.1109/CyberC.2010.64
  • Filename
    5617111