Mining of massive datasets:
Gespeichert in:
| Hauptverfasser: | , , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Cambridge
Cambridge University Press
2020
|
| Ausgabe: | Third edition |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | xi, 553 Seiten Diagramme |
| ISBN: | 9781108476348 |
Internformat
MARC
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Datensatz im Suchindex
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|---|---|
| adam_text | Contents Preface 1 Data Mining 1.1 1.2 1.3 1.4 1.5 1.6 2 MapReduce and the New Software Stack 2.1 2.2 2.3 2.4 2.5 2.6 2.7 2.8 3 What is Data Mining? Statistical Limits on Data Mining Things Useful to Know Outline of the Book Summary of Chapter 1 References for Chapter 1 Distributed File Systems MapReduce Algorithms Using MapReduce Extensions to MapReduce The Communication-Cost Model Complexity Theory for MapReduce Summary of Chapter 2 References for Chapter 2 Finding Similar Items 3.1 3.2 3.3 3.4 3.5 3.6 3.7 3.8 3.9 3.10 3.11 Applications of Set Similarity Shingling of Documents Similarity-Preserving Summaries of Sets Locality-Sensitive Hashing for Documents Distance Measures The Theory of Locality-Sensitive Functions LSH Families for Other Distance Measures Applications of Locality-Sensitive Hashing Methods for High Degrees of Similarity Summary of Chapter 3 References for Chapter 3 page ix 1 1 5 8 16 18 19 20 21 23 29 40 52 58 72 74 78 79 83 86 96 101 107 112 118 125 133 136
vi Contents Mining Data Streams 4.1 4.2 4.3 4.4 4.5 4.6 4.7 4.8 4.9 The Stream Data Model Sampling Data in a Stream Filtering Streams Counting Distinct Elements in a Stream Estimating Moments Counting Ones in a Window Decaying Windows Summary of Chapter 4 References for Chapter 4 Link Analysis 5.1 5.2 5.3 5.4 5.5 5.6 5.7 PageRank Efficient Computation of PageRank Topic-Sensitive PageRank Link Spam Hubs and Authorities Summary of Chapter 5 References for Chapter 5 Frequent Itemsets 6.1 6.2 6.3 6.4 6.5 6.6 6.7 The Market-Basket Model Market Baskets and the A-Priori Algorithm Handling Larger Datasets in Main Memory Limited-Pass Algorithms Counting Frequent Items in a Stream Summary of Chapter 6 References for Chapter 6 Clustering 7.1 7.2 7.3 7.4 7.5 7.6 7.7 7.8 Introduction to Clustering Techniques Hierarchical Clustering К-means Algorithms The CURE Algorithm Clustering in Non-Euclidean Spaces Clustering for Streams and Parallelism Summary of Chapter 7 References for Chapter 7 Advertising on the Web 8.1 8.2 8.3 8.4 Issues in On-Line Advertising On-Line Algorithms The Matching Problem The Adwords Problem 138 138 142 145 148 151 157 163 165 167 169 169 183 189 193 197 202 205 206 206 213 222 229 235 239 241 243 243 247 256 264 267 271 277 280 282 282 285 288 291
Contents 8.5 8.6 8.7 Adwords Implementation Summary of Chapter 8 References for Chapter 8 Recommendation Systems 9.1 9.2 9.3 9.4 9.5 9.6 9.7 A Model for Recommendation Systems Content-Based Recommendations Collaborative Filtering Dimensionality Reduction The Netflix Challenge Summary of Chapter 9 References for Chapter 9 Mining Social-Network Graphs 10.1 10.2 10.3 10.4 10.5 10.6 10.7 10.8 10.9 10.10 Social Networks as Graphs Clustering of Social-Network Graphs Direct Discovery of Communities Partitioning of Graphs Finding Overlapping Communities Simrank Counting Triangles Neighborhood Properties of Graphs Summary of Chapter 10 References for Chapter 10 Dimensionality Reduction 11.1 11.2 11.3 11.4 11.5 11.6 Eigenvalues and Eigenvectors of Symmetric Matrices Principal-Component Analysis Singular-Value Decomposition CUR Decomposition Summary of Chapter 11 References for Chapter 11 Large-Scale Machine Learning 12.1 12.2 12.3 12.4 12.5 12.6 12.7 12.8 The Machine-Learning Model Perceptrons Support-Vector Machines Learning from Nearest Neighbors Decision Trees Comparison of Learning Methods Summary of Chapter 12 References for Chapter 12 vii 300 304 305 307 307 311 321 327 336 337 338 340 340 345 353 358 365 373 382 388 404 408 410 410 417 423 432 438 440 441 442 449 462 474 482 493 494 496
viii Contents Neural Nets and Deep Learning 13.1 13.2 13.3 13.4 13.5 13.6 13.7 13.8 Index Introduction to Neural Nets Dense Feedforward Networks Backpropagation and Gradient Descent Convolutional Neural Networks Recurrent Neural Networks Regularization Summary of Chapter 13 References for Chapter 13 498 498 504 514 522 531 538 541 542 544
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| author | Leskovec, Jure Rajaraman, Anand Ullman, Jeffrey D. 1942- |
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| discipline | Informatik |
| edition | Third edition |
| format | Book |
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| spelling | Leskovec, Jure Verfasser (DE-588)1077529708 aut Mining of massive datasets Jure Leskovec, Stanford University ; Anand Rajaraman, Rocketship VC ; Jeffrey David Ullman, Stanford Duniversity Third edition Cambridge Cambridge University Press 2020 xi, 553 Seiten Diagramme txt rdacontent n rdamedia nc rdacarrier Big Data (DE-588)4802620-7 gnd rswk-swf Data Mining (DE-588)4428654-5 gnd rswk-swf Big Data (DE-588)4802620-7 s Data Mining (DE-588)4428654-5 s 1\p DE-604 Rajaraman, Anand Verfasser (DE-588)104476662X aut Ullman, Jeffrey D. 1942- Verfasser (DE-588)123598230 aut Digitalisierung UB Passau - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=031841432&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
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| title | Mining of massive datasets |
| title_auth | Mining of massive datasets |
| title_exact_search | Mining of massive datasets |
| title_full | Mining of massive datasets Jure Leskovec, Stanford University ; Anand Rajaraman, Rocketship VC ; Jeffrey David Ullman, Stanford Duniversity |
| title_fullStr | Mining of massive datasets Jure Leskovec, Stanford University ; Anand Rajaraman, Rocketship VC ; Jeffrey David Ullman, Stanford Duniversity |
| title_full_unstemmed | Mining of massive datasets Jure Leskovec, Stanford University ; Anand Rajaraman, Rocketship VC ; Jeffrey David Ullman, Stanford Duniversity |
| title_short | Mining of massive datasets |
| title_sort | mining of massive datasets |
| topic | Big Data (DE-588)4802620-7 gnd Data Mining (DE-588)4428654-5 gnd |
| topic_facet | Big Data Data Mining |
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