Database Support for Data Mining Applications 数据发掘应用的数据库支持

作者简介

Data mining from traditional relational databases as well as from non-traditional ones such as semi-structured data, Web data, and scientific databases housing biological, linguistic, and sensor data has recently become a popular way of discovering hidden knowledge.    This book on database support for data mining is developed to approaches exploiting the available database technology, declarative data mining, intelligent querying, and associated issues, such as optimization, indexing, query processing, languages, and constraints. Attention is also paid to the solution of data preprocessing problems, such as data cleaning, discretization, and sampling.    The 16 reviewed full papers presented were carefully selected from various workshops and conferences to provide complete and competent coverage of the core issues. Some papers were developed within an EC funded project on discovering knowledge with inductive queries.

书籍目录

I  Database Languages and Query Execution　Inductive Databases and Multiple Uses of Frequent Itemsets: the cINQ　Approach　Query Languages Supporting Descriptive Rule Mining: A Comparative　Study　Declarative Data Mining Using SQL3　Towards a Logic Query Language for Data Mining　A Data Mining Query Language for Knowledge Discovery in a　Towards Query Evaluation in Inductive Databases Using Version Spaces 　The GUHA Method, Data Preprocessing and Mining　Constraint Based Mining of First Order Sequences in SeqLogII  Support for KDD-Process　Interactivity, Scalability and Resource Control for Efficient KDD　Support in DBMS　Frequent Itemset Discovery with SQL Using Universal Quantification　Deducing Bounds on the Support of Itemsets　Model-Independent Bounding Of the Supports of Boolean Formulae in　Binary Data　Condensed Representations for Sets of Mining Queries　One-Sided Instance-Based Boundary Sets　Domain tructures in Filtering irrelevant Frequent Patterns　Integrity Constraints over Association RulesAuthor Index

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