1. Hierarchical Clustering
2019/2/17
Machine Learning Hierarchical Clustering
Hierarchical Clustering
J.-S. Roger Jang (張智星)
CSIE Dept., National Taiwan University
...
In this talk, we are going to apply two neural network controller design techniques to fuzzy controllers, and construct the so-called on-line adaptive neuro-fuzzy controllers for nonlinear control systems. We are going to use MATLAB, SIMULINK and Handle Graphics to demonstrate the concept. So you can also get a preview of some of the features of the Fuzzy Logic Toolbox, or FLT, version 2.

2. Hierarchical Clustering
2019/2/17
Hierarchical Clustering
Agglomerative clustering (bottom up)
1. Begin with n clusters; each containing one sample
2. Merge the most similar two clusters into one.
3. Repeat the previous step until done
Divisive clustering (top down)
2019/2/17

3. Distance Functions (1/2)
2019/2/17
Distance Functions (1/2)
Single-linkage algorithm (minimum method)
Complete-linkage algorithm (maximum method)
Average-linkage algorithm (average method)
2019/2/17

4. Distance Functions (2/2)
2019/2/17
Distance Functions (2/2)
Ward’s method (minimum-variance method)
d: the number of features
m: data number of Ci plus Cj
2019/2/17

5. Hierarchical Clustering: Demo
aggHierClusteringAnim.m
Also known as the minimum spanning tree!
2019/2/17

6. Wafer Defect Pattern Analysis
Original:
After clustering:
2019/2/17