STrenD: Subspace Trend Discovery

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== Output files ==
 
== Output files ==
 
For 17 samples of 3196 dimensions, clustering sigma = 0.8, k = 4:
 
For 17 samples of 3196 dimensions, clustering sigma = 0.8, k = 4:
 +
 
1. 3196_17_0.8_clustering.txt: agglomerative clustering result, containing index and feature names;
 
1. 3196_17_0.8_clustering.txt: agglomerative clustering result, containing index and feature names;
 +
 
2. 3196_17_0.8_4_NS.txt: pair-wise neighborhood similarity matrix of feature clusters;
 
2. 3196_17_0.8_4_NS.txt: pair-wise neighborhood similarity matrix of feature clusters;
 +
 
3. Shanbhag.txt:  
 
3. Shanbhag.txt:  
 +
 
4. 3196_17_0.8_4_AutoSelFeatures.txt: selected feature index and names;
 
4. 3196_17_0.8_4_AutoSelFeatures.txt: selected feature index and names;
 +
 
5. data_selected_vis.txt: table of normalized data with selected features for visualization;
 
5. data_selected_vis.txt: table of normalized data with selected features for visualization;
 +
 
6. vis_coordinates.txt: output visualization coordinates after dimension reduction by t-SNE.
 
6. vis_coordinates.txt: output visualization coordinates after dimension reduction by t-SNE.
  
 
== Gallery ==
 
== Gallery ==

Revision as of 16:42, 21 August 2014

Contents

Software Interface

STrendInterface.png

Procedure

Output files

For 17 samples of 3196 dimensions, clustering sigma = 0.8, k = 4:

1. 3196_17_0.8_clustering.txt: agglomerative clustering result, containing index and feature names;

2. 3196_17_0.8_4_NS.txt: pair-wise neighborhood similarity matrix of feature clusters;

3. Shanbhag.txt:

4. 3196_17_0.8_4_AutoSelFeatures.txt: selected feature index and names;

5. data_selected_vis.txt: table of normalized data with selected features for visualization;

6. vis_coordinates.txt: output visualization coordinates after dimension reduction by t-SNE.

Gallery

Personal tools