图书标签: 数据挖掘 mining data DataMining
发表于2024-11-08
Introduction to Data Mining pdf epub mobi txt 电子书 下载 2024
Introduction
Rapid advances in data collection and storage technology have enabled or
ganizations to accumulate vast amounts of data. However, extracting useful
information has proven extremely challenging. Often, traditional data analy
sis tools and techniques cannot be used because of the massive size of a data
set. Sometimes, the non-traditional nature of the data means that traditional
approaches cannot be applied even if the data set is relatively small. In other
situations, the questions that need to be answered cannot be addressed using
existing data analysis techniques, and thus, new methods need to be devel
oped.
Data mining is a technology that blends traditional data analysis methods
with sophisticated algorithms for processing large volumes of data. It has also
opened up exciting opportunities for exploring and analyzing new types of
data and for analyzing old types of data in new ways. In this introductory
chapter, we present an overview of data mining and outline the key topics
to be covered in this book. We start with a description of some well-known
applications that require new techniques for data analysis.
Business Point-of-sale data collection (bar code scanners, radio frequency
identification (RFID), and smart card technology) have allowed retailers to
collect up-to-the-minute data about customer purchases at the checkout coun
ters of their stores. Retailers can utilize this information, along with other
business-critical data such as Web logs from e-commerce Web sites and cus
tomer service records from call centers, to help them better understand the
needs of their customers and make more informed business decisions.
Data mining techniques can be used to support a wide range of business
intelligence applications such as customer profiling, targeted marketing, work
flow management, store layout, and fraud detection. It can also help retailers
Pang-Ning Tan现为密歇根州立大学计算机与工程系助理教授,主要教授数据挖掘、数据库系统等课程。此前,他曾是明尼苏达大学美国陆军高性能计算研究中心副研究员(2002-2003)。
Michael Steinbach 明尼苏达大学计算机与工程系研究员,在读博士。
Vipin Kumar明尼苏达大学计算机科学与工程系主任,曾任美国陆军高性能计算研究中心主任。他拥有马里兰大学博士学位,是数据挖掘和高性能计算方面的国际权威,IEEE会士。
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这本书写得逻辑性比较强,全面,而且我觉得涉及的东西也比较底层,让我们了解一些算法的基本型原理是非常重要的。如果,网上的机器学习相关文章看不懂的话,可以从这本书入手。中文版的只看过一点点,感觉完全没逻辑性,完全没感觉。翻译出来完全就变味了,毕竟是语言习惯上的...
评分主要是一些理论的讲解,对数据挖掘的总体起一个概述的作用,偏向于实际应用的较少!对各种算法也只是简单进行说明,然后进行应用,对于刚刚接触数据挖掘的同学有一些意义 内容涵盖方方面面,对于要深挖某个主题的话需要另找书籍结合阅读
评分它是我关于数据挖掘这一方向的入门书。 书中讲了很多基础的数据挖掘算法,读完以后可以对这些算法的基本思想有个了解。书中的例子也很详尽,还是不错的。 但是研究生期间是指望发论文的,这些算法从学术上来说,只能算基础入门了。至于它们在实际工业应...
评分 评分该书特点:以实例为重,给出了常用算法的伪代码,和《模式识别》、《模式分类》等专著比起来,该书略去了各个定理的证明部分,并通过大量枚举具体的分类实例,来简要说明算法的流程和意义。 根据个人的体验,觉得这本书作为第一本数据挖掘的入门读物是再恰当不过的了。...
Introduction to Data Mining pdf epub mobi txt 电子书 下载 2024