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An Introduction to Mining Big and Complex Data
Published on Jan 31, 20171184 Views
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Chapter list
An Introduction to Mining Big and Complex Data00:00
Mining Big and Complex Data01:42
Data mining: Predictive modelling03:54
Example task: Descriptive vars.; Biomarkers for Alzheimer’s05:09
Example: Decision tree for diagnosis05:43
Predictive modeling: Classification and regression06:29
Big Data: Volume & Velocity07:00
Data streams: Regression10:04
Big Data: Variety -Structured Input10:28
Big Data: Variety -Structured Output11:53
Structured-output prediction13:13
Multi-target prediction13:44
Example MTR task: Target vars.; Clinical scores for Alzheimer’s14:02
Example MTR model14:45
Multi-Target Classification & Multi-Label Classification15:12
Multi-Label Classification Example16:41
Hierarchical multi-label classification17:47
Hierarchical multi-label classification: An example18:35
Time-series prediction19:13
Predicting short time series20:19
Even more complex SOs20:45
Predicting tuples of time-series22:14
The other complexity aspects22:24
Semi-supervised learning: Classification and regression23:22
MAESTRA Applications23:54
A central approach in MAESTRA (but not the only one :-)24:30
Predictive Clustering for Predicting Structured Os25:39
Predictive modeling25:46
Clustering25:54
Predictive clustering26:34
Example predictive clustering tree27:39
Top-Down Induction of Decision Trees28:17
Top-down induction of PCTs28:53
Learning PCTs30:10
Predictive clustering31:20
Distances/variances for SOP tasks31:43
Ensembles of PCTs32:23
EnsemblesofPCTs: Bagging32:59
Random Forests & Feature Ranking33:50
Random Forest Ranking - 134:20
Random Forest Ranking - 234:35
Random forest ranking for SOP34:47
Combination of SOP with other complexity aspects35:01
The MAESTRA foundation & pillars35:30
Semi-supervised Learning of PCTs35:36
Learning PCTs to Predict Structured Osfrom DS35:41
Learning in Networks and Applications of MBCD35:50
Acknowledgements and announcement36:00
ECML PKDD 201736:19