Jure Leskovec, Stanford University - SNAP - Stanford University

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High-dimensional data: Locality Sensitive Hashing Dimensionality reduction Clustering The data is a graph: Link Analysis: PageRank, TrustRank, Hubs & Authorities Machine Learning: kNN, Perceptron, SVM, Decision Trees Data is infinite: Mining data streams Advertising on the Web Applications: Association Rules Recommender systems 3/9/2011 Jure Leskovec, Stanford C246: Mining Massive Datasets 6
Many problems can be expressed as finding “similar” sets: Find near-neighbors in high-D space Distance metrics: Points in ℜ n : L1, L2, Manhattan distance Vectors: Cosine similarity Sets of items: Jaccard similarity, Hamming distance Problem: Find near-duplicate documents 3/9/2011 Jure Leskovec, Stanford C246: Mining Massive Datasets 7
Page 1 and 2: CS246: Mining Massive Datasets Jure
Page 3 and 4: MapReduce Association Rules Findi
Page 5: The crew of the space shuttle Endea
Page 9 and 10: Shingling: convert docs to sets of
Page 11 and 12: ands Hash cols of signature matrix
Page 13 and 14: SciFi Romnce A = U Σ V T - exampl
Page 15 and 16: SciFi Romnce A = U Σ V T - exampl
Page 17 and 18: Hierarchical: Agglomerative (botto
Page 19 and 20: LSH: Find somewhat similar pairs o
Page 21 and 22: Rank nodes using link structure Pa
Page 23 and 24: HITS (Hypertext-Induced Topic Selec
Page 25 and 26: High-dimensional data: Locality Sen
Page 27 and 28: Distance metric: Euclidean How ma
Page 29 and 30: Building decision trees using MapRe
Page 33 and 34: Can’t store the whole stream but
Page 35 and 36: You get to see one input piece at a
Page 39 and 40: Observation: Subsets of a frequent
Page 41 and 42: User-user collaborative filtering
Page 43 and 44: MapReduce Association Rules Aprio
Page 45 and 46: Seminars: InfoSeminar: http://i.st

Jure Leskovec, Stanford University - SNAP - Stanford University

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