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Let's continue our discussion about the applications of the graph entropy concept.
Today I'm going to show how we can re-use the same concept on the document clustering.
What I want to highlight is that through such methodology it's possible to:
In the last post I showed how to extract key words from a text through a principle called graph entropy.
Today I'm going to show another application of the graph entropy in order to extract clusters of key words.
The key words of a document depict the main topic of the content, but if the document is big, often, there are many different sub topics related to the…
I would share with you some early results about a research I'm doing in the field of "graph entropy" applied to text mining problem.
Why Graph Entropy is so important?
Based on the main concept of entropy the following assumptions are true: