Jure Leskovec, Stanford University - SNAP - Stanford University

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SciFi Romnce A = U Σ V T - example: Matrix Alien Serenity Casablanca Amelie 1 1 1 0 0 2 2 2 0 0 1 1 1 0 0 5 5 5 0 0 0 0 0 2 2 0 0 0 3 3 0 0 0 1 1 = 0.18 0 0.36 0 0.18 0 0.90 0 0 0.53 0 0.80 0 0.27 ‘strength’ of SciFi-concept 9.64 0 0 5.29 3/9/2011 Jure Leskovec, Stanford C246: Mining Massive Datasets 14 x x 0.58 0.58 0.58 0 0 0 0 0 0.71 0.71
SciFi Romnce A = U Σ V T - example: Matrix Alien Serenity Casablanca Amelie 1 1 1 0 0 2 2 2 0 0 1 1 1 0 0 5 5 5 0 0 0 0 0 2 2 0 0 0 3 3 0 0 0 1 1 = 0.18 0 0.36 0 0.18 0 0.90 0 0 0.53 0 0.80 0 0.27 SciFi-concept 9.64 0 0 5.29 3/9/2011 Jure Leskovec, Stanford C246: Mining Massive Datasets 15 x movie-to-concept similarity matrix x 0.58 0.58 0.58 0 0 0 0 0 0.71 0.71
Page 1 and 2: CS246: Mining Massive Datasets Jure
Page 3 and 4: MapReduce Association Rules Findi
Page 5 and 6: The crew of the space shuttle Endea
Page 7 and 8: Many problems can be expressed as f
Page 9 and 10: Shingling: convert docs to sets of
Page 11 and 12: ands Hash cols of signature matrix
Page 13: 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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