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Similarity clustering with NCD works best when you can visually inspect the items.. Doesn't this apply to all the clustering algorithms ? And this is what we w
by __Joker 12y ago
Similarity clustering with NCD works best when you can visually inspect the items..
Doesn't this apply to all the clustering algorithms ? And this is what we want to avoid for clustering, because it is not easy to visualize in higher dimensions.
- moconnor 12y agoIt's a particular problem with NCD because you don't always know what the compressor is measuring. At least with the L2 distance on a vector you know that those points were close to each other for a well-defined definition of 'close'. If it is surprising that they are close, you might want to investigate whether your feature selection really makes sense for these items. With NCD the compressor selects its own features in a largely opaque way. This makes it fun to use but difficult to debug!