TY - GEN
T1 - Validating synthetic health datasets for longitudinal clustering
AU - Ghassem Pour, Shima
AU - Maeder, Anthony
AU - Jorm, Louisa
PY - 2013
Y1 - 2013
N2 - ![CDATA[Clustering methods partition datasets into subgroups with some homogeneous properties, with information about the number and particular characteristics of each subgroup unknown a priori. The problem of predicting the number of clusters and quality of each cluster might be overcome by using cluster validation methods. This paper presents such an approach in- corporating quantitative methods for comparison be- tween original and synthetic versions of longitudinal health datasets. The use of the methods is demon- strated by using two di_erent clustering algorithms, K-means and Latent Class Analysis, to perform clus- tering on synthetic data derived from the 45 and Up Study baseline data, from NSW in Australia.]]
AB - ![CDATA[Clustering methods partition datasets into subgroups with some homogeneous properties, with information about the number and particular characteristics of each subgroup unknown a priori. The problem of predicting the number of clusters and quality of each cluster might be overcome by using cluster validation methods. This paper presents such an approach in- corporating quantitative methods for comparison be- tween original and synthetic versions of longitudinal health datasets. The use of the methods is demon- strated by using two di_erent clustering algorithms, K-means and Latent Class Analysis, to perform clus- tering on synthetic data derived from the 45 and Up Study baseline data, from NSW in Australia.]]
UR - http://handle.uws.edu.au:8081/1959.7/540362
UR - http://sim.unisa.edu.au/hikm/index.html
M3 - Conference Paper
SN - 9781921770272
SP - 15
EP - 19
BT - Proceedings of the Sixth Australasian Workshop on Health Informatics and Knowledge Management (HIKM 2013): 29 January - 1 February 2013, University of South Australia, Adelaide, Australia
PB - Australian Computer Society
T2 - Australasian Workshop on Health Information and Knowledge Management
Y2 - 29 January 2013
ER -