The "Bag of Words" data set from the UCI Machine Learning Repository contains five text collections in the form of bags-of-words. The URL for the UCI repository is https://archive.ics.uci.edu/ml/datasets/Bag+of+Words.
Your task is to cluster the documents in these datasets via K-means clustering for different values of K and determine an optimum value of K.
As a similarity measure, use Jaccard index, that measures similarity between two documents based on the overlap of words present in both documents. Note that this changes the underlying model from "bag of words" to "set of words".
The datasets are of different sizes. Report your results on the three smaller datasets (Enron emails, NIPS blog entries, KOS blog entries).
In each of the text collections, each document is summarized as a bag (multiset) of words. The individual documents are identified by document IDs and the words are identified by word IDs.
After some cleaning up, in each collection the vocabulary of unique words has been truncated to only keep words that occurred more than ten times overall in that collection.
For each collection XYZ:
vocab.XYZ.txt is the vocabulary file, listing all words that appear in the collection XYZ, one word per line. Each word has an implicit wordID that is its line number in this file, starting with 1 (the word on line 1 has wordID 1, the word on line 2 has wordID 2, ...)
docword.XYZ.txt lists out the number of times each word in vocab.XYZ.txt occurs in each document (only non-zero counts are recorded).
The file docword.XYZ.txt begins with 3 header lines
D W NNZ
where D is the number of documents in the collection, W is the number of words whose frequency is counted (i.e., W is the number of words in vocab.XYZ.txt) and NNZ is the number of non-zero frequency entries for this collection (i.e., NNZ is 3 less than the number of lines in docword.XYZ.txt).
This is followed by NNZ lines of the form
docID wordID count
where count is the number of time the word with id wordID appears in document with id docID. Remember that only non-zero counts are recorded.
D=39861 W=28102 N=6,400,000 (approx)
D=1500 W=12419 N=1,900,000 (approx)
D=3430 W=6906 N=467714
You can use any programming language, including Python and R. You can make use of standard packages for analytics and machine learning. Clearly document any external packages used by your code.
Submit the following via Moodle, as a Jupyter notebook if you are using Python and as a single archive (zip, tar.gz, …) otherwise:
The code you used to solve the assignment.
If you have voluminous output to report, save it somewhere on the cloud and provide a link.
A short write up describing how your code ran on the data sets: the parameters used, time taken, space required, and anything else of interest.
You may work alone or in groups of two. Each group makes a single submission to Moodle. Use either person's Moodle account to submit. The submission should mention the names of the two partners.
There will be a short oral presentation and question/answer session for each submission.