In today's world of competitive business environment, there is a driving need to extract hidden and potentially meaningful information from large databases for effective decision making. This compact book explores the concept of data mining and discusses various data mining techniques and their applications. It is primarily designed for the students of Computer Science and Engineering, Information Technology, Computer Applications, and Management. Written in a student-friendly style, the book describes the various phases of data mining, architecture of a data mining system, and the types of knowledge that can be mined from databases. It elaborates on different data preprocessing techniques such as cleaning, integration, transformation and reduction. The text then explains the various data mining techniques such as association rule mining, data classification and clustering. The book adopts an algorithm-centric approach presenting various algorithms for these data mining techniques. Finally, the text ends with an exhaustive discussion on multimedia data mining (MDM). It illustrates the concepts with the help of various figures and examples. It provides a summary at the end of each chapter for quick revision of key points. It offers chapter-end questions for self-evaluation.

In today's world of competitive business environment, there is a driving need to extract hidden and potentially meaningful information from large databases for effective decision making. This compact book explores the concept of data mining and discusses various data mining techniques and their applications. It is primarily designed for the students of Computer Science and Engineering, Information Technology, Computer Applications, and Management. Written in a student-friendly style, the book describes the various phases of data mining, architecture of a data mining system, and the types of knowledge that can be mined from databases. It elaborates on different data preprocessing techniques such as cleaning, integration, transformation and reduction. The text then explains the various data mining techniques such as association rule mining, data classification and clustering. The book adopts an algorithm-centric approach presenting various algorithms for these data mining techniques. Finally, the text ends with an exhaustive discussion on multimedia data mining (MDM). It illustrates the concepts with the help of various figures and examples. It provides a summary at the end of each chapter for quick revision of key points. It offers chapter-end questions for self-evaluation.

Sensor networks comprise of a number of sensors installed across a spatially distributed network, which gather information and periodically feed a central server with the measured data. The server monitors the data, issues possible alarms and computes fast aggregates. As data analysis requests may concern both present and past data, the server is forced to store the entire stream. But the limited storage capacity of a server may reduce the amount of data stored on the disk. One solution is to compute summaries of the data as it arrives, and to use these summaries to interpolate the real data. This work introduces a recently defined spatio-temporal pattern, called trend cluster, to summarize, interpolate and identify anomalies in a sensor network. As an example, the application of trend cluster discovery to monitor the efficiency of photovoltaic power plants is discussed. The work closes with remarks on new possibilities for surveillance enabled by recent developments in sensing technology.

This Book Addresses All The Major And Latest Techniques Of Data Mining And Data Warehousing. It Deals With The Latest Algorithms For Discussing Association Rules, Decision Trees, Clustering, Neural Networks And Genetic Algorithms. The Book Also Discusses The Mining Of Web Data, Temporal And Text Data. It Can Serve As A Textbook For Students Of Compuer Science, Mathematical Science And Management Science, And Also Be An Excellent Handbook For Researchers In The Area Of Data Mining And Warehousing.

Packed with more than forty percent new and updated material,this edition shows business managers, marketing analysts, and datamining specialists how to harness fundamental data mining methodsand techniques to solve common types of business problems Each chapter covers a new data mining technique, and then showsreaders how to apply the technique for improved marketing, sales,and customer support The authors build on their reputation for concise, clear, andpractical explanations of complex concepts, making this book theperfect introduction to data mining More advanced chapters cover such topics as how to prepare datafor analysis and how to create the necessary infrastructure fordata mining Covers core data mining techniques, including decision trees,neural networks, collaborative filtering, association rules, linkanalysis, clustering, and survival analysis

Modern education has increased its reach through ICT tools and techniques. To manage educational data with the help of modern artificial intelligence, data and web mining techniques on dedicated cloud or grid platforms for educational institutes can be used. By utilizing data science techniques to manage educational data, the safekeeping, delivery, and use of knowledge can be increased for better quality education. Utilizing Educational Data Mining Techniques for Improved Learning: Emerging Research and Opportunities is a critical scholarly resource that explores data mining and management techniques that promote the improvement and optimization of educational data systems. The book intends to provide new models, platforms, tools, and protocols in data science for educational data analysis and introduces innovative hybrid system models dedicated to data science. Including topics such as automatic assessment, educational analytics, and machine learning, this book is essential for IT specialists, data analysts, computer engineers, education professionals, administrators, policymakers, researchers, academicians, and technology experts.

This edited volume assesses capabilities of data mining algorithms for spatial modeling of natural hazards in different countries based on a collection of essays written by experts in the field. The book is organized on different hazards including landslides, flood, forest fire, land subsidence, earthquake, and gully erosion. Chapters were peer-reviewed by recognized scholars in the field of natural hazards research. Each chapter provides an overview on the topic, methods applied, and discusses examples used. The concepts and methods are explained at a level that allows undergraduates to understand and other readers learn through examples. This edited volume is shaped and structured to provide the reader with a comprehensive overview of all covered topics. It serves as a reference for researchers from different fields including land surveying, remote sensing, cartography, GIS, geophysics, geology, natural resources, and geography. It also serves as a guide for researchers, students, organizations, and decision makers active in land use planning and hazard management.

The leading introductory book on data mining, fully updated and revised! When Berry and Linoff wrote the first edition of Data Mining Techniques in the late 1990s, data mining was just starting to move out of the lab and into the office and has since grown to become an indispensable tool of modern business. This new edition—more than 50% new and revised— is a significant update from the previous one, and shows you how to harness the newest data mining methods and techniques to solve common business problems. The duo of unparalleled authors share invaluable advice for improving response rates to direct marketing campaigns, identifying new customer segments, and estimating credit risk. In addition, they cover more advanced topics such as preparing data for analysis and creating the necessary infrastructure for data mining at your company. Features significant updates since the previous edition and updates you on best practices for using data mining methods and techniques for solving common business problems Covers a new data mining technique in every chapter along with clear, concise explanations on how to apply each technique immediately Touches on core data mining techniques, including decision trees, neural networks, collaborative filtering, association rules, link analysis, survival analysis, and more Provides best practices for performing data mining using simple tools such as Excel Data Mining Techniques, Third Edition covers a new data mining technique with each successive chapter and then demonstrates how you can apply that technique for improved marketing, sales, and customer support to get immediate results.

As the most influential activity for social and economic development of individuals and societies, education is a powerful means of shaping the future. The emergence of physical and digital technologies requires an overhaul that would affect not only the way engineering is approached but also the way education is delivered and designed. Therefore, designing and developing curricula focusing on the competencies and abilities of new generation engineers will be a necessity for sustainable success. Engineering Education Trends in the Digital Era is a critical scholarly resource that examines more digitized ways of designing and delivering learning and teaching processes and discusses and acts upon developing innovative engineering education within global, societal, economic, and environmental contexts. Highlighting a wide range of topics such as academic integrity, gamification, and professional development, this book is essential for teachers, researchers, educational policymakers, curriculum designers, educational software developers, administrators, and academicians.

Dr.V.Vijayalakshmi, Assistant Professor & Head, PG & Research Department of Computer Science, Government Arts College, Ariyalur, Tamil Nadu, India. Mr.R.Senthamizh Selvan, Assistant Professor, PG & Research Department of Computer Science, Government Arts College, Ariyalur, Tamil Nadu, India.