• DocumentCode
    2874404
  • Title

    PDSC: Clustering Object Paths from RFID Data Sets

  • Author

    Deng, Huifang ; Lin, Guosheng

  • Author_Institution
    Sch. of Comput. Sci. & Eng., South China Univ. of Technol., Guangzhou, China
  • Volume
    2
  • fYear
    2009
  • fDate
    18-19 July 2009
  • Firstpage
    541
  • Lastpage
    544
  • Abstract
    Radio Frequency Identification (RFID) is playing a more and more important role in our life. How to analyze and discover knowledge from RFID data sets is an urgent and challenging research field. Each tracking object will form a path when it moves through different locations. We present a novel algorithm called PDSC (Path Division and Segments Clustering) to cluster such path data. Considering that there may be some common segments among paths although the full paths are not so similar in general and the common segments may reveal some interesting patterns, we focus on segments clustering in this paper. Firstly we develop an algorithm to divide paths into segments. Secondly a novel similarity definition and algorithm are proposed to measure the similarity of two path segments. Finally we develop a robust clustering algorithm to discover segment clusters. An experimental system is developed to visualize data in every phase. Experimental results demonstrate that PDSC correctly discovers the common path segments.
  • Keywords
    data mining; pattern clustering; radiofrequency identification; PDSC; Path Division and Segments Clustering; RFID data sets; clustering object paths; data mining; sequence similarity; Algorithm design and analysis; Clustering algorithms; Computer science; Data engineering; Data mining; Data visualization; Frequency; Information processing; Radiofrequency identification; Robustness; RFID data mining; density-based clustering; path clustering; sequence clustering; sequence similarity;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Information Processing, 2009. APCIP 2009. Asia-Pacific Conference on
  • Conference_Location
    Shenzhen
  • Print_ISBN
    978-0-7695-3699-6
  • Type

    conf

  • DOI
    10.1109/APCIP.2009.269
  • Filename
    5197256