Principles of Data Mining (Adaptive Computation and Machine Learning series)

Hand, David J.

ISBN 10: 026208290X / ISBN 13: 9780262082907

Published by MIT Press, 2001

About this Item

Most items will be dispatched the same or the next working day. A copy that has been read but remains in clean condition. All of the pages are intact and the cover is intact and the spine may show signs of wear. The book may have minor markings which are not specifically mentioned. Ex library copy with usual stamps & stickers. Seller Inventory # wbs6871736374

Bibliographic Details

Title: Principles of Data Mining (Adaptive ...

Publisher: MIT Press

Publication Date: 2001

Binding: Hardcover

Condition: Good

About this title

Synopsis:

The first truly interdisciplinary text on data mining, blending the contributions of information science, computer science, and statistics. 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.

Synopsis:

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 rigour. 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 network, 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.