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