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EverCam
Unsupervised Learning Algorithm
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2016-06-24,
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寫筆記
索引
1. Data Mining Essentials – Unsupervised Learning資料探勘初探 - 非監督式學習
2. Data Mining Algorithms資料探勘的演算法
3. Unsupervised Learning非監督式學習
4. Unsupervised Learning
5. Measuring Distance/Similarity in Clustering Algorithms
6. Similarity Measures: More Definitions
7. Clustering
8. k-means for k=6
9. k-Means
10. When do we stop?
11. k-Means
12. When do we stop?
13. k-Means
14. k-Means
社群媒體探勘
1. Introduction
1.1
Introduction - Part 1
1.2
Introduction - Part 2
2. Graph Essentials
2.1
Types of Graphs
2.2
Special Graphs and Special Subgraphs
2.3
Graph Algorithms : Graph/Network Traversal Algorithms
3. Network Measures
3.1
Centrality
3.2
Normalization and EigenVector
3.3
Eigenvector Centrality
3.4
Katz Centrality
3.5
PageRank
3.6
Betweenness Centrality
3.7
Closeness Centrality
3.8
Group Centrality
3.9
Transitivity
3.10
Reciprocity
3.11
Status
3.12
Structural Equivalence
3.13
Regular Equivalence
4. Network Models
4.1
Network Models
4.2
Power Law Distribution
4.3
Clustering Coefficient
4.4
Average Path Length
4.5
Random Graphs
4.6
Evolution of Random Graphs
5. Data Mining Essentials
5.1
Data Mining Essentials
5.2
Data
5.3
Data (Part 2)
5.4
Data (Part 3)
5.5
Data (Part 4)
5.6
Supervised Learning Algorithm
5.7
Unsupervised Learning Algorithm
6. Community Analysis
6.1
Community Analysis
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簡介
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詳細
Book Draft PDF and Powerpoint Slides available at http://dmml.asu.edu/smm
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