Interview Question Java Developers
Country: India
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1. per document: extract document text, remove all tags, format etc.
- Chris July 25, 20172. per document: perform a word stemming and remove filling words (like "a", "I", etc...)
3. create word-id's where each word is an id
4. per document: create a vector of words, where you have per word-id, the number of times
it occures in a document. e.g. {(1, 1), (8,10), (14,2)}
5. perform k-means, which is, find k partitions whereas k is the desired amount
of categories. per category, maybe take the top most used words to describe
it.
k-means works as follows:
1) pick k random categories and then check for each document where it has
the most similiarity to and assign it to this category
2) now for each category re-calculate it's center by adding up the vectors
3) check for each document to which category it's closest and re-asign
it to this category
4) stop if no re-asignment happend in 3) or continue with 2)
5) for similarity you can pick anythin, like the cosine-similarity or
other similarity functions