• DocumentCode
    506539
  • Title

    An improving method of CBR retrieval based on self-organizing map

  • Author

    Hui, Du

  • Author_Institution
    Nanjing Univ. of Aeronaut. & Astronaut., Nanjing, China
  • Volume
    1
  • fYear
    2009
  • fDate
    20-22 Nov. 2009
  • Firstpage
    616
  • Lastpage
    620
  • Abstract
    Case retrieval is the most crucial part in CBR. However, traditional case retrieval methods have many disadvantages on accuracy and efficiency. In order to cope with this problem, an improving method based on self-organizing maps (SOM) was proposed in this paper. Firstly, cluster previous cases into several groups using of SOM networks; secondly, input the new case into SOM networks, and identify the most similar case group according the visual clustering output; finally, to decide the most similar case according similarity. The advantage of this method is cases´ visual clustering result provided by SOM networks greatly facilitating retrieval process and decreasing the retrieval time. Experimental results show that the proposed method may improve the efficiency of case retrieval.
  • Keywords
    case-based reasoning; information retrieval; self-organising feature maps; SOM networks; case retrieval methods; case-based reasoning retrieval; self-organizing map; Blindness; Clustering algorithms; Data visualization; Databases; Displays; Humans; Neural networks; Problem-solving; Self organizing feature maps; Unsupervised learning; Case retrieval; Case-based reasoning (CBR); Self-organizing maps (SOM);
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Intelligent Computing and Intelligent Systems, 2009. ICIS 2009. IEEE International Conference on
  • Conference_Location
    Shanghai
  • Print_ISBN
    978-1-4244-4754-1
  • Electronic_ISBN
    978-1-4244-4738-1
  • Type

    conf

  • DOI
    10.1109/ICICISYS.2009.5357621
  • Filename
    5357621