Data Mining

Jiawei Han is a computer science professor at the University of Illinois at Urbana-Champaign. He is well-known for his work in data mining and database systems, and he has received numerous honors for his contributions to the field, including the 2004 ACM SIGKDD Innovations Award.

Micheline Kamber is a researcher who enjoys writing in simple language. She graduated from Concordia University in Canada with a master's degree in computer science (with a focus on artificial intelligence).

Jian Pei is a Canada Research Chair (Tier 1) in Big Data Science and a Professor in Simon Fraser University's School of Computing Science. He is also an associate member of the Statistics and Actuarial Science Department. He is a well-known leading researcher in data science, big data, data mining, and database systems in general.

The growing volume of data in modern business and science necessitates the development of more complicated and sophisticated instruments. Although developments in data mining technology have made large amounts of data collection considerably easier, the technology is still growing, and there is an ongoing need for innovative techniques and tools to help us translate this data into meaningful information and knowledge.

Great strides have been made in the field of data mining since the last edition's publication. The third edition of Data Mining: Concepts and Techniques not only continues the tradition of providing you with an understanding and application of the theory and practice of discovering patterns hidden in large data sets, but it also focuses on new and important topics in the field, including data warehouses and data cube technology, mining streams, mining social networks, and mining spatial, multimedia, and other complex data. Each chapter is a stand-alone guide to a vital topic, presenting tried-and-true methods and solid implementations that can be used directly or with strategic adjustment against live data. If you want to apply today's most sophisticated data mining techniques to real-world business difficulties, this is the material you need.

• Hundreds of methods and implementation examples are presented in pseudo-code and are suited for use in real-world, large-scale data mining applications.
• Mining object-relational databases, spatial databases, multimedia databases, time-series databases, text databases, the World Wide Web, and applications in various industries are among the advanced subjects covered.
• Gives you a complete, hands-on look at the principles and strategies you'll need to make the most of your data.

Author: Jiawei Han, Micheline Kamber and Jian Pei

Link to buy: https://www.amazon.com/Data-Mining-Concepts-Techniques-Management-dp-0123814790/dp/0123814790

Ratings: 4.3 out of 5 stars (from 235 reviews)

Best Sellers Rank: #432,460 in Books

#115 in Management Information Systems

#178 in Artificial Intelligence (Books)

#249 in Data Mining (Books)