【教程免费下载】数据挖掘：实用机器学习工具与技术 （英文版 .第 4版）

前　　言

The convergence of computing and communication has produced a society that feeds on information. Yet most of the information is in its raw form: data. If data is characterized as recorded facts, then information is the set of patterns, or expec-tations, that underlie the data. There is a huge amount of information locked up in databases—information that is potentially important but has not yet been discov-ered or articulated. Our mission is to bring it forth.
Data mining is the extraction of implicit, previously unknown, and potentially useful information from data. The idea is to build computer programs that sift through databases automatically, seeking regularities or patterns. Strong patterns, if found, will likely generalize to make accurate predictions on future data. Of course, there will be problems. Many patterns will be banal and uninteresting. Others will be spurious, contingent on accidental coincidences in the particular dataset used. And real data is imperfect: some parts will be garbled, some miss-ing. Anything that is discovered will be inexact: there will be exceptions to every rule and cases not covered by any rule. Algorithms need to be robust enough to cope with imperfect data and to extract regularities that are inexact but useful.
Machine learning provides the technical basis of data mining. It is used to extract information from the raw data in databases—information i.e., ideally, expressed in a comprehensible form and can be used for a variety of purposes. The process is one of abstraction: taking the data, warts and all, and inferring whatever structure underlies it. This book is about the tools and techniques of machine learning that are used in practical data mining for finding, and if possible describing, structural patterns in data.
As with any burgeoning new technology that enjoys intense commercial atten-tion, the use of machine learning is surrounded by a great deal of hype in the technical—and sometimes the popular—press. Exaggerated reports appear of the secrets that can be uncovered by setting learning algorithms loose on oceans of data. But there is no magic in machine learning, no hidden power, no alchemy. Instead there is an identifiable body of simple and practical techniques that can often extract useful information from raw data. This book describes these techni-ques and shows how they work.
In many applications machine learning enables the acquisition of structural descriptions from examples. The kind of descriptions that are found can be used for prediction, explanation, and understanding. Some data mining applications focus on prediction: forecasting what will happen in new situations from data that describe what happened in the past, often by guessing the classification of new examples. But we are equally—perhaps more—interested in applications where the result of “learning” is an actual description of a structure that can be used to classify examples. This structural description supports explanation and under-standing as well as prediction. In our experience, insights gained by the user are
of most interest in the majority of practical data mining applications; indeed, this is one of machine learning’s major advantages over classical statistical modeling.
The book explains a wide variety of machine learning methods. Some are ped-agogically motivated: simple schemes that are designed to explain clearly how the basic ideas work. Others are practical: real systems that are used in applica-tions today. Many are contemporary and have been developed only in the last few years.
A comprehensive software resource has been created to illustrate the ideas in the book. Called the Waikato Environment for Knowledge Analysis, or WEKA1 for short, it is available as Java source code at www.cs.waikato.ac.nz/ml/weka. It is a full, industrial-strength implementation of most of the techniques that are covered in this book. It includes illustrative code and working implementations of machine learning methods. It offers clean, spare implementations of the simplest techniques, designed to aid understanding of the mechanisms involved. It also provides a workbench that includes full, working, state-of-the-art implementations of many popular learning schemes that can be used for practical data mining or for research. Finally, it contains a framework, in the form of a Java class library, that supports applications that use embedded machine learning and even the implementation of new learning schemes.
The objective of this book is to introduce the tools and techniques for machine learning that are used in data mining. After reading it, you will understand what these techniques are and appreciate their strengths and applicability. If you wish to experiment with your own data, you will be able to do this easily with the WEKA software. But WEKA is by no means the only choice. For example, the freely available statistical computing environment R includes many machine learning algorithms. Devotees of the Python programming language might look at a popular library called scikit-learn. Modern “big data” frameworks for distrib-uted computing, such as Apache Spark, include support for machine learning. There is a plethora of options for deploying machine learning in practice. This book discusses fundamental learning algorithms without delving into software-specific implementation details. When appropriate, we point out where the algorithms we discuss can be found in the WEKA software. We also briefly introduce other machine learning software for so-called “deep learning” from high-dimensional data. However, most software-specific information is relegated to appendices.
The book spans the gulf between the intensely practical approach taken by trade books that provide case studies on data mining and the more theoretical, principle-driven exposition found in current textbooks on machine learning. (A brief description of these books appears in the Further reading section at the end of chapter: What’s it all about?) This gulf is rather wide. To apply machine learning techniques productively, you need to understand something about how they work; this is not a technology that you can apply blindly and expect to get good results. Different problems yield to different techniques, but it is rarely obvi-ous which techniques are suitable for a given situation: you need to know some-thing about the range of possible solutions. And we cover an extremely wide range of techniques. We can do this because, unlike many trade books, this vol-ume does not promote any particular commercial software or approach. We include a large number of examples, but they use illustrative datasets that are small enough to allow you to follow what is going on. Real datasets are far too large to show this (and in any case are usually company confidential). Our data-sets are chosen not to illustrate actual large-scale practical problems, but to help you understand what the different techniques do, how they work, and what their range of application is.


出版在【华章出版社】 作者： [新西兰 ] 伊恩 H. 威腾（Ian H. Witten）埃贝 .弗兰克（Eibe Frank）马克 A. 霍尔（Mark A. Hall）克里斯多夫 J. 帕尔（Christopher J. Pal）