Knowledge Discovery in the Social Sciences

Knowledge Discovery in the Social Sciences helps readers find valid, meaningful, and useful information. It is written for researchers and data analysts as well as students who have no prior experience in statistics or computer science. Suitable for a variety of classes—including upper-division courses for undergraduates, introductory courses for graduate students, and courses in data management and advanced statistical methods—the book guides readers in the application of data mining techniques and illustrates the significance of newly discovered knowledge. 

Readers will learn to: 
• appreciate the role of data mining in scientific research 
• develop an understanding of fundamental concepts of data mining and knowledge discovery
• use software to carry out data mining tasks
• select and assess appropriate models to ensure findings are valid and meaningful
• develop basic skills in data preparation, data mining, model selection, and validation
• apply concepts with end-of-chapter exercises and review summaries
 

Xiaoling Shu is Professor of Sociology at the University of California, Davis. 

PART I. KNOWLEDGE DISCOVERY AND DATA MINING IN
SOCIAL SCIENCE RESEARCH

Chapter 1. Introduction
Chapter 2. New Contributions and Challenges
 
PART II. DATA PREPROCESSING

Chapter 3. Data Issues
Chapter 4. Data Visualization

PART III. MODEL ASSESSMENT

Chapter 5. Assessment of Models

PART IV. DATA MINING: UNSUPERVISED LEARNING

Chapter 6. Cluster Analysis
Chapter 7. Associations

PART V. DATA MINING: SUPERVISED LEARNING

Chapter 8. Generalized Regression
Chapter 9. Classification and Decision Trees
Chapter 10. Artificial Neural Networks

PART VI. DATA MINING: TEXT DATA AND NETWORK DATA

Chapter 11. Web Mining and Text Mining
Chapter 12. Network or Link Analysis

Index