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来自「This complete matlab for neural network」· 文本 代码 · 共 75 行

发信人: ccipt (北方的狼), 信区: DataMining
标  题: Organization of the Book
发信站: 南京大学小百合站 (Wed Aug 22 10:59:30 2001)


Organization of the Book
　　The book is organized as follows.
　　Chapter 1 provides an introduction to the multidisciplinary field of data 
mining. It discusses the evolutionary path of database technology that has led
 to the need for data mining, and the importance of its application potential.
 The basic architecture of data mining systems is described, and a brief intro
duction to t he concepts of database systems and data warehouses is given. A d
etailed classification of data mining tasks is presented, based on the differe
nt kinds of knowledge to be mined. A classification of data mining systems is 
presented, and major challenges in the field are discussed.
　　Chapter 2 is an introduction to data warehouses and OLAP (On-Line Analytic
al Processing). Topics include the concept of data warehouses and multidimensi
onal databases, the construction of data cubes, the implementation of on-line 
analytical processing, and the relationship between data warehousing and data 
mining.
　　Chapter 3 describes techniques for preprocessing the data prior to mining.
 Methods of data cleaning, data integration and transformation, and data reduc
tion are discussed, including the use of concept hierarchies for dynamic and s
tatic discretization. The automatic generation of concept hierarchies is also 
described. 
　　Chapter 4 introduces the primitives of data mining that define the specifi
cation of a data mining task. It describes a data mining query language (DMQL)
 and pro vides examples of data mining queries. Other languages are also descr
ibed, as well as the construction of graphical user interfaces and data mining
 architecture s.
　　Chapter 5 describes techniques for concept description, including characte
rization and discrimination. An attribute-oriented generalization technique is
 introduced, as well as its different implementations including a generalized 
relation technique and a multidimensional data cube technique. Several forms o
f knowledge presentation and visualization are illustrated. Relevance analysis
 is discussed. Methods for class comparison at multiple abstraction levels and
 methods for the extraction of characteristic rules and discriminant rules wit
h interestingness measurements are presented. In addition, statistical measure
s for descriptive mining are discussed.
　　Chapter 6 presents methods for mining association rules in transaction dat
abases as well as relational databases and data warehouses. It includes a clas
sification of association rules, a presentation of the basic Apriori algorithm
 and its variations, and techniques for mining multilevel association rules, m
ultidimensional association rules, quantitative association rules, and correla
tion rules. A new technique called frequent pattern growth is introduced, whic
h mines frequent patterns without candidate set generation. Strategies for fin
ding interesting rules by constraint-based mining and the use of interestingne
ss measures to focus the rule search are also described.
　　Chapter 7 describes methods for data classification and prediction, includ
ing decision tree induction, Bayesian classification, the neural network techn
ique of backpropagation, k-nearest neighbor classifiers, case-based reasoning,
 genetic algorithms, rough set theory, and fuzzy set approaches. Classificatio
n based on concepts from association rule mining is presented. Methods of regr
ession are introduced, and issues regarding classifier accuracy are discussed.

　　Chapter 8 describes methods of cluster analysis. It first introduces the c
oncept of data clustering and then presents several major data clustering appr
oaches, including partition-based clustering, hierarchical clustering, and mod
el-based clustering. Methods for clustering continuous data, discrete data, an
d data in multidimensional data cubes are presented. The scalability of cluste
ring algorithm s is discussed in detail.
　　Chapter 9 discusses methods for data mining in advanced database systems. 
It includes data mining in object-oriented databases, spatial databases, multi
media databases, time-series databases, text databases, and the World Wide Web
.
　　Finally, in Chapter 10, we summarize the concepts presented in this book a
nd discuss applications of data mining and some challenging research issues.

　　Throughout the text, italic is used to emphasize terms that are defined, w
hile bold is used to highlight main ideas.

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