Paper Info
Title
Fuzzy Dissimilarity based Multidimensional Scaling and its Application to Collaborative Learning Data.
Abstract
Recently, the elucidation of learning activity for human learning systems has gained tremendous interests in many areas including neuroscience, brain sciences, behavioral sciences, and education. The main problems of these data are noise and the large amount data (big data). Multidimensional scaling (MDS) is well known method to capture the similarity of objects in lower dimensional configuration space and latent cognitive factors as the dimensions. How- ever, ordinary MDS is based on the Euclidean distance which often fails to capture the similarity relationship in the lower dimensional space. The main reason for this fault is that data usually does not have significant variance to be captured by the MDS. Therefore, in this study, we exploit the latent classification structure of variables to the distance and propose a new dissimilarity and a new multidimensional scaling based on this dissimilarity. We show a better performance of the proposed method by using a time series log data of mobile learning with the collaboration of several students.
Year
DOI
Venue
2013
10.1016/j.procs.2013.09.308
Procedia Computer Science
Keywords
Field
DocType
multidimensional data,fuzzy clustering,cluster-based correlation,clustering based weighted dissimilarity
Data mining,Fuzzy clustering,Collaborative learning,Multidimensional scaling,Computer science,Euclidean distance,Fuzzy logic,Exploit,Artificial intelligence,Big data,Machine learning,Configuration space
Conference
Volume
ISSN
Citations 
20
1877-0509
1
PageRank 
References 
Authors
0.35
0
2
Name
Order
Citations
PageRank
Mika Sato-Ilic13216.09
Peter Ilic231.05