Xiaoling Shu 
Knowledge Discovery in the Social Sciences 
A Data Mining Approach

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

 

Cuprins

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

Despre autor

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