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Principles of Data Mining (Adaptive Computation and Machine Learning) 在线电子书 pdf 下载 txt下载 epub 下载 mobi 下载 2024

Principles of Data Mining (Adaptive Computation and Machine Learning)
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简体网页||繁体网页
David J. Hand 作者
The MIT Press
译者
2001-08-01 出版日期
584 页数
USD 68.00 价格
Hardcover
Adaptive Computation and Machine Learning 丛书系列
9780262082907 图书编码

Principles of Data Mining (Adaptive Computation and Machine Learning) 在线电子书 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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Principles of Data Mining (Adaptive Computation and Machine Learning) 在线电子书 epub 下载 mobi 下载 pdf 下载 txt 下载 2024

Principles of Data Mining (Adaptive Computation and Machine Learning) 在线电子书 epub 下载 mobi 下载 pdf 下载 txt 下载 2024

Principles of Data Mining (Adaptive Computation and Machine Learning) 在线电子书 pdf 下载 txt下载 epub 下载 mobi 下载 2024


Principles of Data Mining (Adaptive Computation and Machine Learning) 在线电子书 用户评价

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逻辑结构清晰，提纲挈领

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CMU的STAT350课的textbook。

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good on overview, and intuition

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Principles of Data Mining (Adaptive Computation and Machine Learning) 在线电子书 著者简介

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。

Principles of Data Mining (Adaptive Computation and Machine Learning) 在线电子书 图书目录

Principles of Data Mining (Adaptive Computation and Machine Learning) 在线电子书 pdf 下载 txt下载 epub 下载 mobi 在线电子书下载

Principles of Data Mining (Adaptive Computation and Machine Learning) 在线电子书 图书描述

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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