Discovering Knowledge in Data : An Introduction to Data Mining

There is a lot to like about this book, but it has some unfortunate flaws. Note that it is part of a Data Mining trilogy. The other two books are: Data Mining Methods and Models and Data Mining the Web: Uncovering Patterns in Web Content, Structure, and Usage. My initial reaction was more negative as I feel strongly about the issues that this book addresses poorly. However, I find myself turning to this book again and again. I would endorse it highly, but with a caution or two. The very best features of the book are the exceptionally clear explanations of complicated algorithms. In particular, Chapters 6 and 7 and their explanations of Decision Trees and Neural Nets are just perfect for both new and veteran analysts who want to understand what is happening "under the hood". Those chapters are stand-outs, but all of the 80%+ part of the book that describes algorithms in detail (clear, careful, and readable detail) is uniformly excellent. For some readers, it may be the first time that the techniques really make sense to them. Now the flaws. The three book format is, frankly, annoying. The second book and third books are much weaker, but the it was clearly designed as a trilogy, so it is hard to recommend the first to a client without at least implicitly recommending the second. Spending my reading time well is more important to me than my reading budget, but the set of three costs more than $200. Unless you plan on an entire shelf of related books, like me, I can't recommend the entire set. The other flaw is less obvious, and is the one that concerns me the most. Although this book cites Dorian Pyle's excellent book ... it seems to miss the whole point. Data Mining data prep is quite different from data prep for statistics. Although the two areas share a lot in common, and while mastery of statistics is a good thing for data miners, this is one of the differences between the two disciplines. Data cleaning and data reduction are critical, but this book suggests that this is accomplished by the human doing all possible bivariates. Recommendations of factor analysis and log transformations abound, but never with cautions of when that is unnecessary or even a bad idea - something Pyle's book explores. Also, transformations like binning come off as something the analyst does during data exploration, getting it perfect before modeling. Sounds like statistics data prep to me - not data mining data prep. If anyone has ever completed data prep without preliminary modeling, or has modeled without having to revisit data prep, I have never heard of it. If a novice data miner were to take the advice too literally, they could get themselves into trouble. This would be especially true of a reader that is well versed in statistics - there is a predictable set of mistakes awaiting the classically trained on their first data mining project! My advice? There is a lot to benefit from here. All of the "white box" walk through examples are great. Consider buying this book, the Pyle book Data Preparation for Data Mining (The Morgan Kaufmann Series in Data Management Systems), and Berry and Linoff Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management, while skipping the other two in this trilogy. Use this book for the algorithm explanations, but be cautious otherwise. The screen shots and discussion of Clementine may be helpful to you, but note that Clementine 8.5 was used. Team DDU......Page 1 CONTENTS......Page 7 PREFACE......Page 11 1 INTRODUCTION TO DATA MINING......Page 16 What Is Data Mining?......Page 17 Need for Human Direction of Data Mining......Page 19 Cross-Industry Standard Process: CRISP–DM......Page 20 Case Study 1: Analyzing Automobile Warranty Claims: Example of the CRISP–DM Industry Standard Process in Action......Page 23 Fallacies of Data Mining......Page 25 Description......Page 26 Estimation......Page 27 Prediction......Page 28 Classification......Page 29 Clustering......Page 31 Association......Page 32 Case Study 2: Predicting Abnormal Stock Market Returns Using Neural Networks......Page 33 Case Study 3: Mining Association Rules from Legal Databases......Page 34 Case Study 4: Predicting Corporate Bankruptcies Using Decision Trees......Page 36 Case Study 5: Profiling the Tourism Market Using k-Means Clustering Analysis......Page 38 References......Page 39 Exercises......Page 40 Why Do We Need to Preprocess the Data?......Page 42 Data Cleaning......Page 43 Handling Missing Data......Page 45 Identifying Misclassifications......Page 48 Graphical Methods for Identifying Outliers......Page 49 Data Transformation......Page 50 Min–Max Normalization......Page 51 Z-Score Standardization......Page 52 Numerical Methods for Identifying Outliers......Page 53 Exercises......Page 54 Hypothesis Testing versus Exploratory Data Analysis......Page 56 Getting to Know the Data Set......Page 57 Dealing with Correlated Variables......Page 59 Exploring Categorical Variables......Page 60 Using EDA to Uncover Anomalous Fields......Page 65 Exploring Numerical Variables......Page 67 Exploring Multivariate Relationships......Page 74 Selecting Interesting Subsets of the Data for Further Investigation......Page 76 Binning......Page 77 Summary......Page 78 Exercises......Page 79 Data Mining Tasks in Discovering Knowledge in Data......Page 82 Statistical Approaches to Estimation and Prediction......Page 83 Univariate Methods: Measures of Center and Spread......Page 84 Statistical Inference......Page 86 Confidence Interval Estimation......Page 88 Bivariate Methods: Simple Linear Regression......Page 90 Dangers of Extrapolation......Page 94 Prediction Intervals for a Randomly Chosen Value of y Given x......Page 95 Multiple Regression......Page 98 Verifying Model Assumptions......Page 100 Exercises......Page 103 Supervised versus Unsupervised Methods......Page 105 Methodology for Supervised Modeling......Page 106 Bias–Variance Trade-Off......Page 108 Classification Task......Page 110 k-Nearest Neighbor Algorithm......Page 111 Distance Function......Page 114 Simple Unweighted Voting......Page 116 Weighted Voting......Page 117 Quantifying Attribute Relevance: Stretching the Axes......Page 118 k-Nearest Neighbor Algorithm for Estimation and Prediction......Page 119 Choosing k......Page 120 Exercises......Page 121 6 DECISION TREES......Page 122 Classification and Regression Trees......Page 124 C4.5 Algorithm......Page 131 Decision Rules......Page 136 Comparison of the C5.0 and CART Algorithms Applied to Real Data......Page 137 Exercises......Page 141 7 NEURAL NETWORKS......Page 143 Input and Output Encoding......Page 144 Simple Example of a Neural Network......Page 146 Sigmoid Activation Function......Page 149 Gradient Descent Method......Page 150 Back-Propagation Rules......Page 151 Example of Back-Propagation......Page 152 Learning Rate......Page 154 Momentum Term......Page 155 Sensitivity Analysis......Page 157 Application of Neural Network Modeling......Page 158 Exercises......Page 160 Clustering Task......Page 162 Hierarchical Clustering Methods......Page 164 Single-Linkage Clustering......Page 165 Complete-Linkage Clustering......Page 166 Example of k-Means Clustering at Work......Page 168 Application of k-Means Clustering Using SAS Enterprise Miner......Page 173 References......Page 176 Exercises......Page 177 Self-Organizing Maps......Page 178 Kohonen Networks......Page 180 Example of a Kohonen Network Study......Page 181 Application of Clustering Using Kohonen Networks......Page 185 Interpreting the Clusters......Page 186 Cluster Profiles......Page 190 Using Cluster Membership as Input to Downstream Data Mining Models......Page 192 Exercises......Page 193 Affinity Analysis and Market Basket Analysis......Page 195 Data Representation for Market Basket Analysis......Page 197 Support, Confidence, Frequent Itemsets, and the A Priori Property......Page 198 How Does the A Priori AlgorithmWork (Part 1)? Generating Frequent Itemsets......Page 200 How Does the A Priori AlgorithmWork (Part 2)? Generating Association Rules......Page 201 Extension from Flag Data to General Categorical Data......Page 204 J-Measure......Page 205 Application of Generalized Rule Induction......Page 206 When Not to Use Association Rules......Page 208 Do Association Rules Represent Supervised or Unsupervised Learning?......Page 211 Local Patterns versus Global Models......Page 212 Exercises......Page 213 11 MODEL EVALUATION TECHNIQUES......Page 215 Model Evaluation Techniques for the Estimation and Prediction Tasks......Page 216 Error Rate, False Positives, and False Negatives......Page 218 Misclassification Cost Adjustment to Reflect Real-World Concerns......Page 220 Decision Cost/Benefit Analysis......Page 222 Lift Charts and Gains Charts......Page 223 Interweaving Model Evaluation with Model Building......Page 226 Confluence of Results: Applying a Suite of Models......Page 227 Exercises......Page 228 EPILOGUE: "WE'VE ONLY JUST BEGUN"......Page 230 INDEX......Page 232 Annotation Learn Data Mining By Doing Data Miningdata Mining Can Be Revolutionary-but Only When It's Done Right. The Powerful Black Box Data Mining Software Now Available Can Produce Disastrously Misleading Results Unless Applied By A Skilled And Knowledgeable Analyst. Discovering Knowledge In Data: An Introduction To Data Mining Provides Both The Practical Experience And The Theoretical Insight Needed To Reveal Valuable Information Hidden In Large Data Sets. Employing A White Box Methodology And With Real-world Case Studies, This Step-by-step Guide Walks Readers Through The Various Algorithms And Statistical Structures That Underlie The Software And Presents Examples Of Their Operation On Actual Large Data Sets. Principal Topics Include:* Data Preprocessing And Classification* Exploratory Analysis* Decision Trees* Neural And Kohonen Networks* Hierarchical And K-means Clustering* Association Rules* Model Evaluation Techniquescomplete With Scores Of Screenshots And Diagrams To Encourage Graphical Learning, Discovering Knowledge In Data: An Introduction To Data Mining Gives Students In Business, Computer Science, And Statistics As Well As Professionals In The Field The Power To Turn Any Data Warehouse Into Actionable Knowledge. An Instructor's Manual Presenting Detailed Solutions To All The Problems In The Book Is Available Online. An Introduction To Data Mining -- Data Preprocessing -- Exploratory Data Analysis -- Statistical Approaches To Estimation And Prediction -- K-nearest Neighbor Algorithm -- Decision Trees -- Neural Networks -- Hierarchical And K-means Clustering -- Kohonen Networks -- Association Rules -- Model Evaluation Techniques. Daniel T. Larose. Includes Bibliographical References And Index. Also Available In An Electronic Version. Mode Of Access: World Wide Web. Learn Data Mining by doing data mining Data mining can be revolutionary-but only when it's done right. The powerful black box data mining software now available can produce disastrously misleading results unless applied by a skilled and knowledgeable analyst. Discovering Knowledge in Data: An Introduction to Data Mining provides both the practical experience and the theoretical insight needed to reveal valuable information hidden in large data sets. Employing a "white box" methodology and with real-world case studies, this step-by-step guide walks readers through the various algorithms and statistical structures that underlie the software and presents examples of their operation on actual large data sets. Principal topics include: Data preprocessing and classification Exploratory analysis Decision trees Neural and Kohonen networks Hierarchical and k-means clustering Association rules Model evaluation techniques Complete with scores of screenshots and diagrams to encourage graphical learning, Discovering Knowledge in Data: An Introduction to Data Mining gives students in Business, Computer Science, and Statistics as well as professionals in the field the power to turn any data warehouse into actionable knowledge. An Instructor's Manual presenting detailed solutions to all the problems in the book is available online. "Data mining can be revolutionary- but only when it's done right. The powerful black box data mining software now available can produce disastrously misleading results unless applied by a skilled and knowledgeable analyst. Discovering Knowledge in Data: An Introduction to Data Mining provides both the practical experience and the theoretical insight needed to reveal valuable information hidden in large data sets. Employing a "white box" methodology and with real-world case studies, this step-by-step guide walks readers through the various algorithms and statistical structures that underlie the software and presents examples of their operation on actual large data sets. Principal topics include: Data preprocessing and classification; Exploratory analysis; Decision trees; Neural and Kohonen networks; Hierarchical and k-means clustering; Association rules; Model evaluation techniques. Complete with scores of screenshots and diagrams to encourage graphical learning, Discovering Knowledge in Data: An Introduction to Data Mining gives students in Business, Computer Science, and Statistics as well as professionals in the field the power to turn any data warehouse into actionable knowledge."--Page 4 of cover Teach Like a Champion 2.0 is a complete update to the international bestseller. This teaching guide is a must-have for new and experienced teachers alike. Over 700,000 teachers around the world already know how the techniques in this book turn educators into classroom champions. With ideas for everything from classroom management to inspiring student engagement, you will be able to perfect your teaching practice right away. "Complete with scores of screenshots and diagrams to encourage graphical learning, Discovering Knowledge in Data: An Introduction to Data Mining gives students in Business, Computer Science, and Statistics as well as professionals in the field the power to turn any data warehouse into actionable knowledge."--Jacket