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一本评价较高的数据挖掘方面书籍；
书的代码和示例都是Python；
以下是目录：
Preface xiii
1 Introduction to Collective Intelligence 1
What Is Collective Intelligence? 2
What Is Machine Learning? 3
Limits of Machine Learning 4
Real-Life Examples 5
Other Uses for Learning Algorithms 5
2 Making Recommendations 7
Collaborative Filtering 7
Collecting Preferences 8
Finding Similar Users 9
Recommending Items 15
Matching Products 17
Building a delicious Link Recommender 19
Item-Based Filtering 22
Using the MovieLens Dataset 25
User-Based or Item-Based Filtering? 27
Exercises 28
3 Discovering Groups 29
Supervised versus Unsupervised Learning 29
Word Vectors 30
Hierarchical Clustering 33
Drawing the Dendrogram 38
Column Clustering 40
K-Means Clustering 42
Clusters of Preferences 44
Viewing Data in Two Dimensions 49
Other Things to Cluster 53
Exercises 53
4 Searching and Ranking 54
What’s in a Search Engine? 54
A Simple Crawler 56
Building the Index 58
Querying 63
Content-Based Ranking 64
Using Inbound Links 69
Learning from Clicks 74
Exercises 84
5 Optimization 86
Group Travel 87
Representing Solutions 88
The Cost Function 89
Random Searching 91
Hill Climbing 92
Simulated Annealing 95
Genetic Algorithms 97
Real Flight Searches 101
Optimizing for Preferences 106
Network Visualization 110
Other Possibilities 115
Exercises 116
6 Document Filtering 117
Filtering Spam 117
Documents and Words 118
Training the Classifier 119
Calculating Probabilities 121
A Na?ve Classifier 123
The Fisher Method 127
Persisting the Trained Classifiers 132
Filtering Blog Feeds 134
Improving Feature Detection 136
Using Akismet 138
Alternative Methods 139
Exercises 140
7 Modeling with Decision Trees 142
Predicting Signups 142
Introducing Decision Trees 144
Training the Tree 145
Choosing the Best Split 147
Recursive Tree Building 149
Displaying the Tree 151
Classifying New Observations 153
Pruning the Tree 154
Dealing with Missing Data 156
Dealing with Numerical Outcomes 158
Modeling Home Prices 158
Modeling “Hotness” 161
When to Use Decision Trees 164
Exercises 165
8 Building Price Models 167
Building a Sample Dataset 167
k-Nearest Neighbors 169
Weighted Neighbors 172
Cross-Validation 176
Heterogeneous Variables 178
Optimizing the Scale 181
Uneven Distributions 183
Using Real Data—the eBay API 189
When to Use k-Nearest Neighbors 195
Exercises 196
9 Advanced Classification: Kernel Methods and SVMs 197
Matchmaker Dataset 197
Difficulties with the Data 199
Basic Linear Classification 202
Categorical Features 205
Scaling the Data 209
Understanding Kernel Methods 211
Support-Vector Machines 215
Using LIBSVM 217
Matching on Facebook 219
Exercises 225
10 Finding Independent Features 226
A Corpus of News 227
Previous Approaches 231
Non-Negative Matrix Factorization 232
Displaying the Results 240
Using Stock Market Data 243
Exercises 248
11 Evolving Intelligence 250
What Is Genetic Programming? 250
Programs As Trees 253
Creating the Initial Population 257
Testing a Solution 259
Mutating Programs 260
Crossover 263
Building the Environment 265
A Simple Game 268
Further Possibilities 273
Exercises 276
12 Algorithm Summary 277
Bayesian Classifier 277
Decision Tree Classifier 281
Neural Networks 285
Support-Vector Machines 289
k-Nearest Neighbors 293
Clustering 296
Multidimensional Scaling 300
Non-Negative Matrix Factorization 302
Optimization 304
A Third-Party Libraries 309
B Mathematical Formulas 316
Index 323
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