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Online Clustering of High-Dimensional Trajectories under Concept Drift
Published on Nov 30, 20113188 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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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