Principles of data mining / David Hand, Heikki Mannila, Padhraic Smyth.
- 作者： Hand, D. J.
- 其他作者：
- 其他題名：
- Adaptive computation and machine learning
- 出版： Cambridge, Mass. : MIT Press 2001.
- 叢書名： Adaptive computation and machine learning
- 主題： Data mining.
- ISBN： 026208290X (hbk.) :: US$62.00
- 一般註："A Bradford book."
- 書目註：Includes bibliographical references (p. [491]-524) and index.
-
讀者標籤：
- 系統號： 005110142 | 機讀編目格式
館藏資訊
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.