TY - GEN
T1 - ReDSOM
T2 - 8th IEEE International Conference on Data Mining, ICDM 2008
AU - Denny, null
AU - Williams, Graham J.
AU - Christen, Peter
PY - 2008
Y1 - 2008
N2 - We introduce a Self-Organizing Map (SOM) based visualization method that compares cluster structures in temporal datasets using Relative Density SOM (ReDSOM) visualization. Our method, combined with a distance matrix-based visualization, is capable of visually identifying emerging clusters, disappearing clusters, enlarging clusters, contracting clusters, the shifting of cluster centroids, and changes in cluster density. For example, when a region in a SOM becomes significantly more dense compared to an earlier SOM, and well separated from other regions, then the new region can be said to represent a new cluster. The capabilities of ReDSOM are demonstrated using synthetic datasets, as well as real-life datasets from the World Bank and the Australian Taxation Office. The results on the real-life datasets demonstrate that changes identified interactively can be related to actual changes. The identification of such cluster changes is important in many contexts, including the exploration of changes in population behavior in the context of compliance and fraud in taxation.
AB - We introduce a Self-Organizing Map (SOM) based visualization method that compares cluster structures in temporal datasets using Relative Density SOM (ReDSOM) visualization. Our method, combined with a distance matrix-based visualization, is capable of visually identifying emerging clusters, disappearing clusters, enlarging clusters, contracting clusters, the shifting of cluster centroids, and changes in cluster density. For example, when a region in a SOM becomes significantly more dense compared to an earlier SOM, and well separated from other regions, then the new region can be said to represent a new cluster. The capabilities of ReDSOM are demonstrated using synthetic datasets, as well as real-life datasets from the World Bank and the Australian Taxation Office. The results on the real-life datasets demonstrate that changes identified interactively can be related to actual changes. The identification of such cluster changes is important in many contexts, including the exploration of changes in population behavior in the context of compliance and fraud in taxation.
UR - http://www.scopus.com/inward/record.url?scp=67049100143&partnerID=8YFLogxK
U2 - 10.1109/ICDM.2008.34
DO - 10.1109/ICDM.2008.34
M3 - Conference contribution
AN - SCOPUS:67049100143
SN - 9780769535029
T3 - Proceedings - IEEE International Conference on Data Mining, ICDM
SP - 173
EP - 182
BT - Proceedings - 8th IEEE International Conference on Data Mining, ICDM 2008
Y2 - 15 December 2008 through 19 December 2008
ER -