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Data mining : concepts and techniques / Jiawei Han, Micheline Kamber, Jian Pei. Amsterdam ; Boston : Elsevier/Morgan Kaufmann, 2012.1 online resource (xxxv, 703 pages) : illustrations, ; 29 cm.Not only does the third of edition of Data Mining: Concepts and Techniques continue the tradition of equipping you with an understanding and application of the theory and practice of discovering patterns hidden in large data sets, it also focuses on new, important topics in the field: data warehouses and data cube technology, mining stream, mining social networks, and mining spatial, multimedia and other complex data. Each chapter is a stand-alone guide to a critical topic, presenting proven algorithms and sound implementations ready to be used directly or with strategic modification against live data. This is the resource you need if you want to apply today's most powerful data mining techniques to meet real business challenges.
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The elements of statistical learning : data mining, inference, and prediction / Trevor Hastie, Robert Tibshirani, J H Friedman. New York : Springer, 2009.xxii, 739 p. : ill. (some color), charts ; 29 cm.During the past decade there has been an explosion in computation and information technology. With it have come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. The challenge of understanding these data has led to the development of new tools in the field of statistics, and spawned new areas such as data mining, machine learning, and bioinformatics. Many of these tools have common underpinnings but are often expressed with different terminology. This book describes the important ideas in these areas in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of color graphics. It is a valuable resource for statisticians and anyone interested in data mining in science or industry. The book's coverage is broad, from supervised learning (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting---the first comprehensive treatment of this topic in any book.
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Truyền thông đa phương tiện = Multimedia communication / Lê Đắc Nhường, Nguyễn Gia Như. Hà Nội : Thông tin và Truyền thông, 2016.411tr.: hình vẽ ; 24cm.Trình bày những kiến thức cơ bản về truyền thông đa phương tiện gồm: Một số khái niệm cơ bản; môi trường truyền dẫn; hệ thống truyền tin; kiến trúc và mô hình truyền thông; mã hoá và điều chế số liệu; xử lí dữ liệu đa phương tiện; giao thức điều khiển đường truyền và giao thức đa phương tiện
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