数据挖掘：实用机器学习技术及Java实现（英文版）作者:[新西兰]Ian H.Witten

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书名:数据挖掘：实用机器学习技术及Java实现（英文版）
作者:[新西兰]Ian H.Witten
译者:
出版社:机械工业出版社
价格:40元

简介

本书是综合运用数据挖掘、数据分析、信息理论通讯机器学习技术的里程碑。
Lan H.Witten，新西兰怀卡托大学计算机科学系教授。他是ACM和新西兰皇家学会的成员，并参加了英国、美国、加拿大和新西兰的专业计算、信息检索、工程等协会。他著有多部著作，是多家技术杂志的作者，发表过大量论文。

这是一本将数据挖掘算法和数据挖掘实践完美结合起来的优秀教材。作者以其丰富的经验，对数据挖掘的概念和数据挖掘所有的技术（特别是机器学习）进行了深入浅出的介绍，并对应用机器学习工具进行数据挖掘给出了良好的建议。数据挖掘中的各个关键要素也事例融合在众多实例中加以介绍。
本书还介绍了Weka这种基于Java的软件系统。该软件系统可以用来分析数据集，找到适用的模式，进行正确的分析，也可以用来开发自己的机器学方案。
本书的主要特点：
解释数据挖掘算法的原理。
通过实例帮助读者根据实际情况选?窈鲜实乃惴ǎ⒈冉虾推拦啦煌椒ǖ贸龅慕峁?
介绍提高性能的技术，包括数据处理以及组合不同方法得到的输出。
提供了本书所有的Weka软件和附加学习材料，可以从http://www.mkp.com/datamining上下载这些资料。

Foreword
Preface
1  Whats  it  all  about?
  1.1  Data  mining  and  machine  learning
  1.2  Simple  examples:The  weather  problem  and  others
  1.3  Fielded  application
  1.4  Machine  learning  and  statistics
  1.5  Generalization  as  search
  1.6  Data  mining  and  ethics
  1.7  Further  reading
2  Input:Concepts,instances,attributes
  2.1  Whats  a  concept?
  2.2  Whats  in  an  example?
  2.3  Whats  in  an  attribute?
  2.4  Preparing  the  input
  2.5  Further  reading
3  Output:Knowledge  representation
  3.1  Decision  tables
  3.2  Decision  trees
  3.3  Classification  rules
  3.4  Association  rules
  3.5  Rules  with  exceptions
  3.6  Rules  involving  relations
  3.7  Trees  for  numeric  prediction
  3.8  Instance-based  representation
  3.9  Clusters
  3.10  Further  reading
4  Algorithms:The  basic  methods
  4.1  Infereing  rudimentary  rules
  4.2  Statistical  modeling
  4.3  Divide  and  conuquer:Constructing  decision  trees
  4.4  Covering  algorithms:Construsting  rules
  4.5  Mining  association  rules
  4.6  Linear  models
  4.7  Instance-based  learning
  4.8  Further  reading
5  Credibility:Evaluation  whats  been  learnde
  5.1  Training  and  testing
  5.2  predicting  per  formance
  5.3  Cross-vaidation
  5.4  Other  estimates
  5.5  Comparing  data  mining  schems
  5.6  Predicting  Probabilities
  5.7  Counting  the  cost
  5.8  Evaluating  numer  ic  prediction
  5.9  The  minimum  deScription  length  principle
  5.10  Applying  MDL  to  clustering
  5.11  Further  reading
6  Implemententation:Real  machine  learning  schemes
  6.1  Decision  tress
  6.2  Classification  rules
  6.3  Extending  linear  classification:Support  vector  machines
  6.4  Instance-based  learning
  6.5  Numeric  prediction
  6.6  Clustering
7  Moving  on:Engineering  the  input  and  output
  7.1  Attribute  selection
  7.2  Discretizing  numeric  attributes
  7.3  Automtic  data  cleansing
  7.4  Combining  multiple  models
  7.5  Further  reading
8  Nuts  and  bolts:Machine  learning  algorithms  in  Java
  8.1  Getting  started
  8.2  Javadoc  and  the  class  library
  8.3  Processing  dataset  using  the  machine  learning  programs
  8.4  Embedded  machine  learning
  8.5  Writing  new  learning  schemes
9  Looking  forward
  9.1  learning  from  massive  datasets
  9.2  Visualizing  machine  learning
  9.3  Incorporation  domain  knowlgdge
  9.4  Text  mining
  9.5  Mining  the  World  Wide  Web
  9.6  Further  reading
References
Index
About  the  authors

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