Contents

### **Preface XI**



Chapter 9 **Identification of Research Thematic Approaches Based on Keywords Network Analysis in Colombian Social Sciences 145** José Hernando Ávila-Toscano, Ivón Catherine Romero-Pérez, Ailed Marenco-Escuderos and Eugenio Saavedra Guajardo

Preface

scriptive or predictive.

ecology, rainfall, and soil.

This book on data mining discusses a broad set of ideas and presents some of the advanced research in this field. The book is triggered by pervasive applications that retrieve knowledge from real-world big data. It provides the basics, methods, and tools in processing large data available with various applications. The chapters discuss various applications and research frontiers in data mining with algorithms and implementation details for use in real-world. The inundation of data from various fields like scientific observations, medical data, social science data, financial data, business data, etc. has been an issue in the recent past and is expected to become worse in future. Manual analysis of this large volume of data is difficult and there is a need to automate the data analysis. Data mining is the intelligent supporting techniques in discovering useful information and knowledge from the big data. This can be through characterization, classification, discrimination, anomaly detection, association, clus‐ tering, trend or evolution prediction, etc., and accordingly, data mining can be either de‐

Researchers and practitioners in areas such as statistics, pattern recognition, machine learn‐ ing, artificial intelligence, data analytics, and visualization are contributing to the field of data mining for better utilization of the data. Data mining finds applications in the entire spectrum of science and technology including basic sciences to life sciences and medicine, to

This book on data mining consists of ten chapters. The chapters include the real-world prob‐ lems in various fields and propose methods to address each one of them. Technologies ex‐

In Chapter I, the ensemble methods in environmental data mining are discussed. Environ‐ mental data mining is the nontrivial process of identifying valid, novel, and potentially use‐ ful patterns in data from environmental sciences. This chapter proposes ensemble methods like bagging, random forest, boosting, and voting in environmental data mining that com‐ bine the outputs from multiple classification models to obtain better results than the outputs that could be obtained with a single model. In the experimental studies, ensemble methods are tested on different real-world environmental datasets in various subjects such as air,

In order to maintain a good relationship with customers in business or with workers in a production field, there is always the need to collect and analyze the data and interpret the information. Data mining can play an important part in such scenarios as seen in Chapters II and III. Chapter II discusses the estimation of customer lifetime value using machine learn‐ ing techniques. The chapter includes the passenger network value assessment model for the

social, economic, and cognitive sciences, to engineering and computers.

plored in each of these chapters are introduced for the reader in every chapter.

Chapter 10 **Data Privacy for Big Data Publishing Using Newly Enhanced PASS Data Mining Mechanism 165** Priyank Jain, Manasi Gyanchandani and Nilay Khare
