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Data Mining of Massive Datasets (3rd ed) book links

• Data Mining of Massive Datasets, 3rd ed

  • Contents

    • Definitions; statistical limits; useful concepts;

  • MapReduce

    • Distributed file systems; MapReduce; algorithms; extensions; communications-cost model; complexity theory

  • Finding Similar Items

    • Set similarity; document shingling; summaries & similarity preservation; locality-sensitive hashing; distance measures; locality-sensitive function theory; LSH & other metrics; LSH applications; methods for high degrees of similarity

  • Mining Streams

    • Data model; sampling; filtering; counting distinct elements; estimating moments; counting “ones” in a window; decaying windows

  • Link Analysis

    • PageRank; PageRank computation; topic-sensitive PageRank; link spam; hubs & authorities

  • Frequent Itemsets

    • Market-basket model; A-Priori algorithm; large datasets & main memory; limited-pass algorithms; counting frequent items in a stream

  • Clustering

    • Intro; hierarchical; K-means; CURE algorithm; non-Euclidean clustering; streams & parallelism

  • Web Advertising

    • Issues; online algorithms; matching; Adwords problem; Adwords implementation

  • Recommenders

    • Model; content-based; collaborative; dimensionality reduction

  • Social Network Graph Mining

    • Social nets as graphs; clustering; community discovery; graph partitions; overlapping communities; Simrank; counting triangles; neighborhood properties

  • Dimensionality Reduction

    • Eigenvalues & eigenvectors of symmetric matrices; principal component analysis; singular value decomposition; CUR decomposition

  • Scaling

    • Model; perceptrons; support-vector machines; nearest neighbors; decision trees; comparison of methods

  • Neural Nets

    • Intro; dense feedforward nets; backprop; gradient descent; convolutional nets; recurrent nets; regularization