Loading in 5 sec....

Neural Network Homework Report: Clustering of the Self-Organizing MapPowerPoint Presentation

Neural Network Homework Report: Clustering of the Self-Organizing Map

Download Presentation

Neural Network Homework Report: Clustering of the Self-Organizing Map

Loading in 2 Seconds...

- 108 Views
- Uploaded on
- Presentation posted in: General

Neural Network Homework Report: Clustering of the Self-Organizing Map

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.

- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -

Neural Network Homework Report:Clustering of the Self-Organizing Map

IEEE TRANSACTIONS ON NEURAL NETWORKS, VOL.11, NO.3, MAY 2000

Professor：Hahn-Ming Lee

Student : Hsin-Chung Chen

M9315928

- INTRODUCTION
- CLUSTERING
- SOM CLUSTERING
- EXPERIMENTS
- CONCLUSION

- DATA mining processes
- problem definition
- data acquisition.
- data preprocessing and survey
- data modeling
- evaluation.
- knowledge deployment.

- Self-organization map feature:
- Dimensionality reduction of unsupervised learning
- Can applied in deal huge amounts of sample
- The original data set is represented using a smaller set of prototype vectors
- not to find an optimal clustering but to get good

- two main ways approaches
- hierarchical approaches
- agglomerative algorithm：
- bottom-up strategies to build a hierarchical clustering tree

- divisive algorithm：
- top-down strategies to build a hierarchical clustering tree

- agglomerative algorithm：
- partitive approaches
- k-means

- hierarchical approaches
- optimal clustering is a partitioning
- minimizes distances within
- maximizes distances between clusters

- SOM training
- first to find the best matching unit (BMU)
- the prototype vectors are updated.

- The SOM algorithm characteristic：
- applicable to large data sets.
- The computational complexity scales linearly with the number of data samples
- it does not require huge amounts of memorythat basically just the prototype vectors and the current training vector .

- Tools: SOM_ToolBox 2.0 :
- Data set:clown.dat
- Data set (“clown.data”) consisted of 2220 2-D samples.
- cluster with three subclusters (right eye)
- spherical cluster (left eye)
- elliptical cluster (nose)
- nonspherical cluster (U-shaped: mouth)
- large and sparse cluster (body)
- noise .(such as black x)

- Data set (“clown.data”) consisted of 2220 2-D samples.

- Methods and Parameters:
- Cluster step 1:
- Training Parameters of the SOM's
- Map size: 19x17Initial Neighborhood Widths: Rough Phases σ1(0): 10 Fine-Tuning Phases σ2(0): 2learning rates:(The learning rate decreased linearly to zero during the training) Rough Phases : 0.5 Fine-Tuning Phases 0.05

- Cluster step 2:
- Method: K-MeansUsing 100 Runs

- Cluster step 1:

Conclusion