The Top Ten Algorithms in Data Mining 在线电子书 pdf 下载 txt下载 epub 下载 mobi 下载 2024

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Xindong Wu 作者

Chapman and Hall/CRC

译者

2009-4-9 出版日期

232 页数

USD 82.95 价格

Hardcover

丛书系列

9781420089646 图书编码

The Top Ten Algorithms in Data Mining 在线电子书图书标签: 数据挖掘算法机器学习 Algorithms 计算机 DM 美国理工

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The Top Ten Algorithms in Data Mining 在线电子书 epub 下载 mobi 下载 pdf 下载 txt 下载 2024

The Top Ten Algorithms in Data Mining 在线电子书 pdf 下载 txt下载 epub 下载 mobi 下载 2024

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5年之后回头看，江山哪曾变一分。

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Data Mining 快速进阶

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5年之后回头看，江山哪曾变一分。

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读过其中的几章，写的比较浅显易懂。具体算法很好的入门书。

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读过其中的几章，写的比较浅显易懂。具体算法很好的入门书。

The Top Ten Algorithms in Data Mining 在线电子书著者简介

The Top Ten Algorithms in Data Mining 在线电子书图书目录

The Top Ten Algorithms in Data Mining 在线电子书 pdf 下载 txt下载 epub 下载 mobi 在线电子书下载

The Top Ten Algorithms in Data Mining 在线电子书图书描述

The Best-Known Algorithms Currently Used in the Data Mining Community

Contributions from recognized leaders in the field

Identifying some of the most influential algorithms that are widely used in the data mining community, The Top Ten Algorithms in Data Mining provides a description of each algorithm, discusses its impact, and reviews current and future research. Thoroughly evaluated by independent reviewers, each chapter focuses on a particular algorithm and is written by either the original authors of the algorithm or world-class researchers who have extensively studied the respective algorithm.

The book concentrates on the following important algorithms: C4.5, k-Means, SVM, Apriori, EM, PageRank, AdaBoost, kNN, Naive Bayes, and CART. Examples illustrate how each algorithm works and highlight its overall performance in a real-world application. The text covers key topics—including classification, clustering, statistical learning, association analysis, and link mining—in data mining research and development as well as in data mining, machine learning, and artificial intelligence courses.

By naming the leading algorithms in this field, this book encourages the use of data mining techniques in a broader realm of real-world applications. It should inspire more data mining researchers to further explore the impact and novel research issues of these algorithms.

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