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Online Clustering of High-Dimensional Trajectories under Concept Drift
Published on 2011-11-303194 Views
Historical transaction data are collected in many applications, e.g., patient histories recorded by physicians and customer transactions collected by companies. An important question is the learning o
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Presentation
Online Clustering of High-Dimensional Trajectories under Concept Drift00:00
Outline - 100:15
Outline - 200:35
CRM Application - 100:36
CRM Application - 201:54
Clustering Trajectories under Drift: Objective - 102:31
Clustering Trajectories under Drift: Objective - 202:58
Clustering Trajectories under Drift: Objective - 302:58
Clustering Trajectories under Drift: Objective - 403:19
Clustering Trajectories under Drift - 103:34
Clustering Trajectories under Drift - 203:50
EM Trajectory Clustering03:50
Outline - 304:53
TRACER Algorithm - 105:01
TRACER Algorithm - 205:40
Kalman fi lter - 105:45
Kalman fi lter - 206:29
TRACER Initialisation - 107:20
TRACER Initialisation - 207:59
TRACER Initialisation - 308:03
TRACER Initialisation - 408:45
TRACER Initialisation - 508:54
TRACER Initialisation - 609:08
TRACER Update and Clustering09:41
Outline - 410:49
Objective - 110:51
Objective - 211:34
Update Strategies12:11
Measure12:12
Accuracy of State Estimation over Time12:13
Dependence of Purity : Shift and Speed : Dataset Size - 113:23
Dependence of Purity : Shift and Speed : Dataset Size - 213:25
Outline - 515:23
Conclusion - 115:25
Conclusion - 216:40
Conclusion - 317:45