问题
So, I've been mulling over these concepts for some time, and my understanding is very basic. Information retrieval seems to be a topic seldom covered in the wild...
My questions stem from the process of clustering documents. Let's say I start off with a collection of documents containing only interesting words. What is the first step here? Parse the words from each document and create a giant 'bag-of-words' type model? Do I then proceed to create vectors of word counts for each document? How do I compare these documents using something like the K-means clustering?
回答1:
Try Tf-idf for starters.
If you read Python, look at
"Clustering text documents using MiniBatchKmeans"
in scikit-learn:
"an example showing how the scikit-learn can be used to cluster
documents by topics using a bag-of-words approach".
Then feature_extraction/text.py in the source has very nice classes.
来源：https://stackoverflow.com/questions/8057442/document-clustering-basics