Детальная информация

Название: Principles of data mining
Авторы: Hand David; Mannila Heikki; Smyth Padhraic
Организация: IEEE Xplore (Online Service); MIT Press
Выходные сведения: Cambridge, Massachusetts London, England: MIT Press: A Bradford book, 2001
Коллекция: Электронные книги зарубежных издательств; MIT Press eBooks Library; Общая коллекция
Тематика: Базы данных; интеллектуальный анализ данных
УДК: 004.6
Тип документа: Другой
Тип файла: Другой
Язык: Английский
Права доступа: Доступ по паролю из сети Интернет (чтение, печать)
Ключ записи: 6267275

Разрешенные действия: Посмотреть

Аннотация

The growing interest in data mining is motivated by a common problem across disciplines: how does one store, access, model, and ultimately describe and understand very large data sets? Historically, different aspects of data mining have been addressed independently by different disciplines. This is the first truly interdisciplinary text on data mining, blending the contributions of information science, computer science, and statistics.The book consists of three sections. The first, foundations, provides a tutorial overview of the principles underlying data mining algorithms and their application. The presentation emphasizes intuition rather than rigor. The second section, data mining algorithms, shows how algorithms are constructed to solve specific problems in a principled manner. The algorithms covered include trees and rules for classification and regression, association rules, belief networks, classical statistical models, nonlinear models such as neural networks, and local "memory-based" models. The third section shows how all of the preceding analysis fits together when applied to real-world data mining problems. Topics include the role of metadata, how to handle missing data, and data preprocessing.

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