[公告]David Hand, Heikki Mannila and Padhraic Smyth:Principle of Data Mining

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Principle of Data Mining By David Hand, Heikki Mannila and Padhraic Smith

The MIT Press@ 2001(546) pages

A comprehensive, highly technical look at the math and science behind extracting useful information from large databases.

Chapter 1: Introduction Chapter 2: Measurement and Data Chapter 3: Visualizing and Exploring Data Chapter 4: Data Analysis and Uncertainty Chapter 5: A systematic Overviews of Data Mining Algorithm Chapter 6: Models and Patterns Chapter 7: Score Functions for Data Mining Algorithm Chapter 8: Search and Optimization Method Chapter 9: Descriptive Modeling Chapter 10: Predictive Modeling for Classification Chapter 11: Predictive Modeling for Regression Chapter 12: Data Organization and Databases Chapter 13: Finding Pattern and Rules Chapter 14: Retrieval by Content Appendix: Random Variables Reference Index List of the Figures List of the Tables

If you want the book abovementioned, please let me know..

[此贴子已经被作者于2005-1-12 11:39:33编辑过]

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关键词：Data Mining principle David Mann ning David Mining Data principle Hand

原书名： Principles of Data Mining [原书信息]  
原出版社： Massachusetts Institute of Technology  
作者： David Hand,Heikki Mannila,Padhraic Smyth  [作者简介]  
译者： 张银奎 廖丽 宋俊 等  [译者简介]  
书号： 7-111-11577-5
页码： 361
开本： 16开 版次：1-1  
丛书名： 计算机科学丛书  
出版社： 机械工业出版社  
出版日期： 2003-4-1

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.

以下是引用hanszhu在2005-1-12 10:34:34的发言：

Principle of Data Mining By David Hand, Heikki Mannila and Padhraic Smith

The MIT Press@ 2001(546) pages

A comprehensive, highly technical look at the math and science behind extracting useful information from large databases.

Chapter 1: Introduction Chapter 2: Measurement and Data Chapter 3: Visualizing and Exploring Data Chapter 4: Data Analysis and Uncertainty Chapter 5: A systematic Overviews of Data Mining Algorithm Chapter 6: Models and Patterns Chapter 7: Score Functions for Data Mining Algorithm Chapter 8: Search and Optimization Method Chapter 9: Descriptive Modeling Chapter 10: Predictive Modeling for Classification Chapter 11: Predictive Modeling for Regression Chapter 12: Data Organization and Databases Chapter 13: Finding Pattern and Rules Chapter 14: Retrieval by Content Appendix: Random Variables Reference Index List of the Figures List of the Tables

If you want the book abovementioned, please let me know..

This one? 9410.rar (3.24 MB) 本附件包括：

• Principles of Data Mining.pdf

2005-2-28 00:45:00 上传

[此贴子已经被作者于2005-2-28 0:46:11编辑过]

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