en-de
en-es
en-fr
en-sl
en
en-zh
0.25
0.5
0.75
1.25
1.5
1.75
2
State of the Art in Data Stream Mining
Published on Jan 29, 200810846 Views
Related categories
Chapter list
State-of-the-Art in Data Stream Mining (Part I)00:00
Index01:57
Outline02:33
Scenario part102:37
Scenario part202:57
Illustrative Learning Tasks part104:25
Illustrative Learning Tasks part204:55
Illustrative Learning Tasks part305:14
The Data Stream Phenomenon08:06
Outline27:36
Introduction part128:46
Main Algorithm [Rodrigues, Gama, 2006]33:27
Feeding ODAC34:43
Similarity Distance36:18
Splitting Criteria36:58
Expanding a Leaf41:44
Multiple Time-Windows41:46
Change Detection42:50
Properties of ODAC44:53
A snapshot - 1 year data, 2500 variables46:11
Memory Usage46:29
Speed in Processing Time47:18
Outline47:37
Desirable properties47:48
Very Fast Decision Trees48:59
Very Fast Decision Trees: Main Algorithm50:44
Classification Strategies51:55
VFDT: Illustrative Evaluation54:22
VFDT: Analysis55:27
Neural-Nets and Data Streams56:53
Introduction part201:01:11
The Nature of Change01:01:41
Change Detection in Predictive Learning01:03:15
A Framework based on Statistical Quality Control01:04:49
The P-chart Algorithm part101:06:06
The P-chart Algorithm part201:08:44
Analysis of the P-chart Algorithm01:09:51
Main Characteristics in Change Detection01:10:10
Decision model management01:11:12
Dynamic Weighted Majority01:12:00
Granularity of Decision Models01:13:36
Outline01:14:48
Thanks for your attention!01:19:29