很好的挖掘模型和open source软件：Mining Mart
采用了用例(case) 和２层抽象数据模型的方法，前者让用户经验／流程模版化并且可以复用，后者则很好地从抽象概念映射到数据库表字段
http://www.springer.com/cda/content/document/cda_downloaddocument/9783540406778-c1.pdf?SGWID=0-0-45-124187-p18192908
As depicted in Fig. 3.1, there are di®erent kinds of experts working at di®erent
ends of a knowledge discovery process. First of all a domain expert will de¯ne
a conceptual data model using a concept editor. The entities involved in data
mining are made explicit by this expert. The conceptual model of M4 is about
concepts having features, and relationships between these concepts.
Examples for concepts are Customer and Product. Although at the current
stage of development concepts refer to either database views or tables, they
should rather be considered as part of a more abstract model of the domain.
Concepts consist of features, either base attributes or multi-column features.
A base attribute corresponds to a single database attribute, e.g., the name
of a customer. A multi-column feature is a feature containing a ¯xed set of
base attributes. This kind of feature should be used when information is split
over multiple base attributes. An example is to de¯ne a single multi-column
feature for the amount and the currency of a bank transfer, which are both
represented by base attributes.
Relationships are connections between concepts. There could be a rela-
tionship named Buys between the concepts Customer and Product, for exam-
ple. At the database level, one-to-many relationships are represented by for-
eign key references and many-to-many relationships make use of cross tables.
However, these details are hidden from the user at the abstract conceptual
level.
To organize concepts and relationships, the M4 model o®ers the oppor-
tunity to use inheritance. Modelling the domain in this fashion, the concept
Customer could have subconcepts like Private Customer and Business Customer.
Subconcepts inherit all features of their superconcept. The relationship Buys
could for instance have a subrelationship Purchases on credit.
http://www.springer.com/cda/content/document/cda_downloaddocument/9783540406778-c1.pdf?SGWID=0-0-45-124187-p18192908
As depicted in Fig. 3.1, there are di®erent kinds of experts working at di®erent
ends of a knowledge discovery process. First of all a domain expert will de¯ne
a conceptual data model using a concept editor. The entities involved in data
mining are made explicit by this expert. The conceptual model of M4 is about
concepts having features, and relationships between these concepts.
Examples for concepts are Customer and Product. Although at the current
stage of development concepts refer to either database views or tables, they
should rather be considered as part of a more abstract model of the domain.
Concepts consist of features, either base attributes or multi-column features.
A base attribute corresponds to a single database attribute, e.g., the name
of a customer. A multi-column feature is a feature containing a ¯xed set of
base attributes. This kind of feature should be used when information is split
over multiple base attributes. An example is to de¯ne a single multi-column
feature for the amount and the currency of a bank transfer, which are both
represented by base attributes.
Relationships are connections between concepts. There could be a rela-
tionship named Buys between the concepts Customer and Product, for exam-
ple. At the database level, one-to-many relationships are represented by for-
eign key references and many-to-many relationships make use of cross tables.
However, these details are hidden from the user at the abstract conceptual
level.
To organize concepts and relationships, the M4 model o®ers the oppor-
tunity to use inheritance. Modelling the domain in this fashion, the concept
Customer could have subconcepts like Private Customer and Business Customer.
Subconcepts inherit all features of their superconcept. The relationship Buys
could for instance have a subrelationship Purchases on credit.
posted by hunter at 5:01 AM
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