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
Link To Document :
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