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Spatio-temporal data mining: Part 2
Published on Jan 31, 20171200 Views
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Chapter list
Spatio-Temporal Data Mining (Part II)00:00
Goals - 100:10
Goals - 201:15
Assumed Background01:36
Outline01:50
Sensor networks02:02
From sensor networks to geophysical time series02:32
Applications02:57
Online data03:08
Data scenario03:25
Tasks - 104:11
Issues & challenges04:49
Additional issues & challenges05:26
Tasks - 206:13
Summarization - 107:32
Summarization - 207:48
Summarization - 308:38
Summarization in sensor network anaysis - 109:19
Summarization in sensor network anaysis - 210:24
Trend cluster (spatial+temporal)11:24
Trend cluster discovery - 111:54
Trend cluster discovery - 212:35
South-America air climate - 112:53
Intel Berkeley Lab13:30
Further work14:06
In-network trend cluster discovery15:04
South-America air climate - 216:12
Interpolation16:25
Interpolation - spatial - 116:50
Interpolation - spatial - 217:52
Interpolation – spatio-temporal - 118:48
Interpolation – spatio-temporal - 219:08
Interpolation – trend cluster - 119:46
Interpolation – trend cluster - 220:41
Interpolation – trend cluster - 321:09
South America air climate21:44
Anomaly/Change detection22:16
Anomaly detection22:23
Change detection22:41
Anomaly and change detection23:43
Anomaly and change detection - trend cluster - 124:39
Anomaly and change detection - trend cluster - 225:45
Anomaly and change detection - trend cluster - 325:49
Forecasting25:52
Time series analysis26:08
Spatial-aware forecasting - 127:10
Spatial-aware forecasting - 227:51
Spatial-aware ARIMA28:21
Spatial-aware forecasting - 328:44
Spatial-aware forecasting - 429:19
Multivariate case29:43
State of the art - spatial - 129:52
State of the art - spatial - 230:44
State of the art - spatial - 331:30
Time-evolving interpolative clustering31:50
Open challenges32:01
Thank you for the attention32:52