HAN AND KAMBER DATA MINING EBOOK PDF

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.

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Our ability to generate and collect data has been increasing rapidly. Not only are all of our business, scientific, and government transactions now computerized, but the widespread use of digital cameras, publication tools, and bar codes also generate data. On the collection side, scanned text and image platforms, satellite remote sensing systems, and the World Wide Web have flooded us with a tremendous amount of data.

This explosive growth has generated an even more urgent need for new techniques and automated tools that can help us transform this data into useful information and knowledge. Like the first edition, voted the most popular data mining book by KD Nuggets readers, this book explores concepts and techniques for the discovery of patterns hidden in large data sets, focusing on issues relating to their feasibility, usefulness, effectiveness, and scalability. However, since the publication of the first edition, great progress has been made in the development of new data mining methods, systems, and applications.

This new edition substantially enhances the first edition, and new chapters have been added to address recent developments on mining complex types of data— including stream data, sequence data, graph structured data, social network data, and multi-relational data. Database professionals and researchers, data mining professionals; undergraduate and graduate students. He is also an associate member of the Department of Statistics and Actuarial Science. He is a well-known leading researcher in the general areas of data science, big data, data mining, and database systems.

His expertise is on developing effective and efficient data analysis techniques for novel data intensive applications. Micheline Kamber is a researcher with a passion for writing in easy-to-understand terms. She has a master's degree in computer science specializing in artificial intelligence from Concordia University, Canada. We are always looking for ways to improve customer experience on Elsevier. We would like to ask you for a moment of your time to fill in a short questionnaire, at the end of your visit.

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However, due to transit disruptions in some geographies, deliveries may be delayed. Paperback ISBN: Imprint: Morgan Kaufmann. Published Date: 1st March Page Count: Sorry, this product is currently unavailable. Sorry, this product is currently out of stock. Institutional Subscription. Instructor Ancillary Support Materials. Free Shipping Free global shipping No minimum order. Chapter 1: Introduction 1.

Why Is It Important? What Is Prediction? A comprehensive, practical look at the concepts and techniques you need to know to get the most out of real business data Updates that incorporate input from readers, changes in the field, and more material on statistics and machine learning Dozens of algorithms and implementation examples, all in easily understood pseudo-code and suitable for use in real-world, large-scale data mining projects Complete classroom support for instructors at www.

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Your review was sent successfully and is now waiting for our team to publish it. Reviews 1. Sort: Select. Updating Results. Verified Reviewer. Data Mining: Concepts and Techniques. Review by Dr. All the concepts are explained with numerical. On Data Mining: Concepts and Techniques. Was this review helpful?

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Data Mining: Concepts and Techniques

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.

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