Mining of Massive Datasets notes (1st edition)

Summary of the theory for Advances in Data Mining for the Computer Science master at Leiden University. Covers tokenization, Recommender Systems (Naive methods, Collaborative filtering, Matrix Factorization), RBM, Similarity Search (shingling, minhashing, LSH) and Pagerank (Markov process, RageRank algorithm) It is missing the part about Support Vector machines, the last bit of minhasing and the part of the DGIM algorithm for data streams and. Parts missing are labeled with a colored marking.

Summary of the theory for Advances in Data Mining for the Computer Science master at Leiden University. Covers tokenization, Recommender Systems (Naive methods, Collaborative filtering, Matrix Factorization), RBM, Similarity Search (shingling, minhashing, LSH) and Pagerank (Markov process, RageRank algorithm) It is missing the part about Support Vector machines, the last bit of minhasing and the part of the DGIM algorithm for data streams and. Parts missing are labeled with a colored marking.