Data Mining: Concepts and Techniques provides the concepts and techniques in processing gathered data or information, which will be used in various applications. Specifically, it explains data mining and the tools used in discovering knowledge from the collected data. This book is referred as the knowledge discovery from data (KDD). It focuses on the feasibility, usefulness, effectiveness, and scalability of techniques of large data sets. After describing data mining, this edition explains the methods of knowing, preprocessing, processing, and warehousing data. It then presents information about data warehouses, online analytical processing (OLAP), and data cube technology. Then, the methods involved in mining frequent patterns, associations, and correlations for large data sets are described. The book details the methods for data classification and introduces the concepts and methods for data clustering. The remaining chapters discuss the outlier detection and the trends, applications, and research frontiers in data mining.
This book is intended for Computer Science students, application developers, business professionals, and researchers who seek information on data mining.
Data mining originated primarily from researchers running into challenges posed by new data sets. Data mining is not a new area, but has re-emerged as data science because of new data sources such as Big Data. This course focuses on defining both data mining and data science and provides a review of the concepts, processes, and techniques used. “We are living in the data deluge age. The Data Mining: Concepts and Techniques shows us how to find useful knowledge in all that data. This Third Edition significantly expands the core chapters on data preprocessing, frequent pattern mining, classification, and clustering.
Presents dozens of algorithms and implementation examples, all in pseudo-code and suitable for use in real-world, large-scale data mining projects
Addresses advanced topics such as mining object-relational databases, spatial databases, multimedia databases, time-series databases, text databases, the World Wide Web, and applications in several fields
Provides a comprehensive, practical look at the concepts and techniques you need to get the most out of your data