Paper Info
Title
Unsupervised case memory organization: analysing computational time and soft computing capabilities
Abstract
There are problems that present a huge volume of information or/and complex data as imprecision and approximated knowledge. Consequently, a Case-Based Reasoning system requires two main characteristics. The first one consists of offering a good computational time without reducing the accuracy rate of the system, specially when the response time is critical. On the other hand, the system needs soft computing capabilities in order to construct CBR systems more tractable, robust and tolerant to noise. The goal of this paper is centred on achieving a compromise between computational time and complex data management by focusing on the case memory organization (or clustering) through unsupervised techniques. In this sense, we have adapted two approaches: 1) neural networks (Kohonen Maps); and 2) inductive learning (X-means). The results presented in this work are based on datasets acquired from medical and telematics domains, and also from UCI repository.
Year
DOI
Venue
2006
10.1007/11805816_19
ECCBR
Keywords
Field
DocType
response time,accuracy rate,case-based reasoning system,kohonen maps,cbr system,complex data management,unsupervised case memory organization,complex data,computational time,uci repository,soft computing capability,good computational time,neural network,clustering,soft computing,case base reasoning,kohonen map
Inductive logic programming,Computer science,Self-organizing map,Unsupervised learning,Artificial intelligence,Soft computing,Cluster analysis,Reasoning system,Case-based reasoning,Artificial neural network,Machine learning,Distributed computing
Conference
Volume
ISSN
ISBN
4106
0302-9743
3-540-36843-4
Citations 
PageRank 
References 
14
0.79
28
Authors
4
Name
Order
Citations
PageRank
A. Fornells1393.57
Elisabet Golobardes220620.16
David Vernet3275.32
G. Corral4332.42