Maybe this is my last post for 202 blog.
I have taken statistical learning theory course at EECS dept in this semester. This course provides an introduction to the area of probabilistic models, and requires students to do a final project. I picked up an unsupervised clustering of UC Berkeley courses based on their descriptions.
The problem background for this task is as follows. You know, UC Berkeley provides an online courses search system, but it is a very low-level. It only provides users to search by course name, instructor name, department etc. But we can’t search courses keywords in course descriptions.
I beleive, first of all, that it should provide a keyword search system for course descriptions. Also, it is desireble for the system to be equipped with “recommendation systems”, which provides students course lists that may probably be suitable for them, based on their course registered histories(it is maybe like Amazon’s recommendation system, one kind of “folksonomy” to clssify courses).
To implement a recommendation system based on students’ course registered histories, courses in Berkeley need to be clustered by the student registerd histories in an unsupervised manner.
I can’t utilize students’ registered history. So, I utilize online course descriptions in the current system for the substitution and try to cluster UC Berkeley courses based on these course descriptions by a mixture mutlivariate Bernoulli distribution probabilistic model with EM algorithm(in detail, please refer a text “Introduction to Information Retrieval”, pp338-pp340.), and testify whether I can reasonably and explainably cluster UC Berkeley’s courses in unsupervised manner.
The result is as follows. I tested to categorize Math+Information+Statistics+Economics+Computer Science department courses(total 226 courses) into 7 clusters. Several categories of classes generated by the algorithm are explainable such as “Statistical/Mathematical methology-oriented course cluster”, “Programming related course cluster”, “Individual study/Seminar related course cluster” and “Economics related(but less statitics oriented) course cluster) “.
Of course, not all courses are explanale by these labels. But, basically, although I applied very basic methods without special information-retrieval methods such as lemmatization, stemming and removing stop words, results are better than I expected before conducting the experiment.
1st cluster courses
2nd cluster courses
3rd cluseter courses
4th cluster courses
This is a back-envelope simulation and result is simple. Also, there are some problems. But it can show a certain result, and, more imporantly, this is an integrated task with Info202(Information organization and retrieval), Info206(network programming and Java) and CS281A(statistical learning theory, esp. EM algorithm) for me. I am satisfied with the result and the fact that I achieve ability to implement this simulation by myself in a short time.
