图书介绍
Mining of massive datasetsPDF|Epub|txt|kindle电子书版本下载
- Anand Rajaraman ; Jeffrey D. Ullman 著
- 出版社： Cambridge University Press
- ISBN：1107015357
- 出版时间：2012
- 标注页数：316页
- 文件大小：39MB
- 文件页数：328页
- 主题词：
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图书目录
1 Data Mining1
1.1 What is Data Mining？1
1.2 Statistical Limits on Data Mining4
1.3 Things Useful to Know7
1.4 Outline of the Book15
1.5 Summary of Chapter 116
1.6 References for Chapter 117
2 Large-Scale File Systems and Map-Reduce18
2.1 Distributed File Systems18
2.2 Map-Reduce21
2.3 Algorithms Using Map-Reduce26
2.4 Extensions to Map-Reduce37
2.5 Efficiency of Cluster-Computing Algorithms42
2.6 Summary of Chapter 249
2.7 References for Chapter 251
3 Finding Similar Items53
3.1 Applications of Near-Neighbor Search53
3.2 Shingling of Documents57
3.3 Similarity-Preserving Summaries of Sets60
3.4 Locality-Sensitive Hashing for Documents67
3.5 Distance Measures71
3.6 The Theory of Locality-Sensitive Functions77
3.7 LSH Families for Other Distance Measures83
3.8 Applications of Locality-Sensitive Hashing88
3.9 Methods for High Degrees of Similarity96
3.10 Summary of Chapter 3104
3.11 References for Chapter 3106
4 Mining Data Streams108
4.1 The Stream Data Model108
4.2 Sampling Data in a Stream112
4.3 Filtering Streams115
4.4 Counting Distinct Elements in a Stream118
4.5 Estimating Moments122
4.6 Counting Ones in a Window127
4.7 Decaying Windows133
4.8 Summary of Chapter 4136
4.9 References for Chapter 4137
5 Link Analysis139
5.1 PageRank139
5.2 Efficient Computation of PageRank153
5.3 Topic-Sensitive PageRank159
5.4 Link Spam163
5.5 Hubs and Authorities167
5.6 Summary of Chapter 5172
5.7 References for Chapter 5175
6 Frequent Itemsets176
6.1 The Market-Basket Model176
6.2 Market Baskets and the A-Priori Algorithm183
6.3 Handling Larger Datasets in Main Memory192
6.4 Limited-Pass Algorithms199
6.5 Counting Frequent Items in a Stream205
6.6 Summary of Chapter 6209
6.7 References for Chapter 6211
7 Clustering213
7.1 Introduction to Clustering Techniques213
7.2 Hierarchical Clustering217
7.3 K-means Algorithms226
7.4 The CURE Algorithm234
7.5 Clustering in Non-Euclidean Spaces237
7.6 Clustering for Streams and Parallelism241
7.7 Summary of Chapter 7247
7.8 References for Chapter 7250
8 Advertising on the Web252
8.1 Issues in On-Line Advertising252
8.2 On-Line Algorithms255
8.3 The Matching Problem258
8.4 The Adwords Problem261
8.5 Adwords Implementation270
8.6 Summary of Chapter 8273
8.7 References for Chapter 8275
9 Recommendation Systems277
9.1 A Model for Recommendation Systems277
9.2 Content-Based Recommendations281
9.3 Collaborative Filtering291
9.4 Dimensionality Reduction297
9.5 The NetFlix Challenge305
9.6 Summary of Chapter 9306
9.7 References for Chapter 9308
Index310