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Network Applications
Published on Jan 31, 2017990 Views
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Chapter list
Mining Networked Data (Part 2: Applications)00:00
Mining network data: different dimensions00:20
Using PPI network autocorrelation in hierarchical multi-label classification trees for gene function prediction00:33
Gene Function Prediction: a Structured Output Prediction Tasks01:04
DIP Yeast network (A PPI network)01:24
The Basic Idea: learning NHMC02:55
Autocorrelation measure03:23
Autocorrelation for the HMC setting04:20
Algorithm outline04:33
Experiments: Gene Ontology04:59
Conclusion06:15
Semi-Supervised Multi-View Learning for Gene Network Reconstruction06:39
Inducing GRNs from expression data08:44
Measures and Scoring Schemes08:49
Exploiting multiple Measures and Schemes08:54
Learning to combine scores09:20
The algorithm10:59
Experiments - Datasets11:49
Experiments - Competitors12:03
Wilcoxon signed rank test12:09
Conclusion12:38
Predictive Modeling of PV Energy Production13:25
Renewable energy14:13
The data mining task - 114:48
The data mining task - 215:40
Data collection and loading16:20
Spatial autocorrelation - 116:29
Spatial autocorrelation - 217:34
Temporal autocorrelation17:57
The learning phase - 119:24
The learning phase - 219:49
The learning phase - 320:20
The learning phase - 420:25
The learning phase - 520:52
The learning phase - 621:01
Datasets21:08
Evaluation procedure21:16
Table V21:40
Experimental results22:53
Conclusions23:04
FORCE: A Forecasting-by-Clustering Ensemble Learning Algorithm for Big Data Streams23:19
Background: Density-based clustering23:35
Idea24:37
Algorithm outline25:34
Similarity measure26:09
Clustering step26:35
Forecasting step27:35
FORCE: Forecasting results28:05
Scalability28:19
Conclusions28:46
Global Conclusions29:21