Discovering knowledge in data : an introduction to data mining / Daniel T. Larose.

Format:

Book

Author/Creator:

Larose, Daniel T.

Language:

English

Subjects (All):

Data mining.

Physical Description:

1 online resource (240 p.)

Edition:

1st ed.

Place of Publication:

Hoboken, N.J. : Wiley-Interscience, c2005.

Language Note:

English

Summary:

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

Contents:

DISCOVERING KNOWLEDGE IN DATA; CONTENTS; PREFACE; 1 INTRODUCTION TO DATA MINING; What Is Data Mining?; Why Data Mining?; Need for Human Direction of Data Mining; Cross-Industry Standard Process: CRISP-DM; Case Study 1: Analyzing Automobile Warranty Claims: Example of the CRISP-DM Industry Standard Process in Action; Fallacies of Data Mining; What Tasks Can Data Mining Accomplish?; Description; Estimation; Prediction; Classification; Clustering; Association; Case Study 2: Predicting Abnormal Stock Market Returns Using Neural Networks; Case Study 3: Mining Association Rules from Legal Databases

Case Study 4: Predicting Corporate Bankruptcies Using Decision TreesCase Study 5: Profiling the Tourism Market Using k-Means Clustering Analysis; References; Exercises; 2 DATA PREPROCESSING; Why Do We Need to Preprocess the Data?; Data Cleaning; Handling Missing Data; Identifying Misclassifications; Graphical Methods for Identifying Outliers; Data Transformation; Min-Max Normalization; Z-Score Standardization; Numerical Methods for Identifying Outliers; References; Exercises; 3 EXPLORATORY DATA ANALYSIS; Hypothesis Testing versus Exploratory Data Analysis; Getting to Know the Data Set

Dealing with Correlated VariablesExploring Categorical Variables; Using EDA to Uncover Anomalous Fields; Exploring Numerical Variables; Exploring Multivariate Relationships; Selecting Interesting Subsets of the Data for Further Investigation; Binning; Summary; References; Exercises; 4 STATISTICAL APPROACHES TO ESTIMATION AND PREDICTION; Data Mining Tasks in Discovering Knowledge in Data; Statistical Approaches to Estimation and Prediction; Univariate Methods: Measures of Center and Spread; Statistical Inference; How Confident Are We in Our Estimates?; Confidence Interval Estimation

Bivariate Methods: Simple Linear RegressionDangers of Extrapolation; Confidence Intervals for the Mean Value of y Given x; Prediction Intervals for a Randomly Chosen Value of y Given x; Multiple Regression; Verifying Model Assumptions; References; Exercises; 5 k-NEAREST NEIGHBOR ALGORITHM; Supervised versus Unsupervised Methods; Methodology for Supervised Modeling; Bias-Variance Trade-Off; Classification Task; k-Nearest Neighbor Algorithm; Distance Function; Combination Function; Simple Unweighted Voting; Weighted Voting; Quantifying Attribute Relevance: Stretching the Axes

Database Considerationsk-Nearest Neighbor Algorithm for Estimation and Prediction; Choosing k; Reference; Exercises; 6 DECISION TREES; Classification and Regression Trees; C4.5 Algorithm; Decision Rules; Comparison of the C5.0 and CART Algorithms Applied to Real Data; References; Exercises; 7 NEURAL NETWORKS; Input and Output Encoding; Neural Networks for Estimation and Prediction; Simple Example of a Neural Network; Sigmoid Activation Function; Back-Propagation; Gradient Descent Method; Back-Propagation Rules; Example of Back-Propagation; Termination Criteria; Learning Rate; Momentum Term

Sensitivity Analysis

Notes:

Description based upon print version of record.

Includes bibliographical references and index.

ISBN:

1-280-27529-4

9786610275298

0-470-36135-2

0-471-68753-7

0-471-68754-5

OCLC:

271807869