Principles of Data Mining (Adaptive Computation and Machine Learning)

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David Hand是伦敦帝国大学数学系统计学教授。Heikki Mannila是赫尔辛基工业大学计算科学与工程系的教授,诺基亚研究中心的研究员。Padhraic Smyth是加州大学Irvine分校信息与计算科学系的副教授。

出版者:The MIT Press
作者:David J. Hand
出品人:
页数:584
译者:
出版时间:2001-08-01
价格:USD 68.00
装帧:Hardcover
isbn号码:9780262082907
丛书系列:Adaptive Computation and Machine Learning
图书标签:
  • 数据挖掘 
  • datamining 
  • 机器学习 
  • textbook 
  • MIT 
  • 统计 
  • 模式识别 
  • Statistics 
  •  
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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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good on overview, and intuition

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逻辑结构清晰,提纲挈领

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逻辑结构清晰,提纲挈领

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