XML Data Mining: Models, Methods, and Applications

The widespread use of XML in business and scientific databases has prompted the development of methodologies, techniques, and systems for effectively managing and analyzing XML data. This has increasingly attracted the attention of different research communities, including database, information retrieval, pattern recognition, and machine learning, from which several proposals have been offered to address problems in XML data management and knowledge discovery.

XML Data Mining: Models, Methods, and Applications aims to collect knowledge from experts of database, information retrieval, machine learning, and knowledge management communities in developing models, methods, and systems for XML data mining. This book addresses key issues and challenges in XML data mining, offering insights into the various existing solutions and best practices for modeling, processing, analyzing XML data, and for evaluating performance of XML data mining algorithms and systems.

The many academic areas covered in this publication include, but are not limited to:

• Approximate Matching of XML Documents and Schemas
• Association Rule Mining of XML Data
• Classification of XML Data
• Clustering of XML Data
• Domain-specific XML Mining Applications: Credit Risk Assessment, Social Network User Modeling of Geographical Maps, P2P systems
• Frequent Pattern Discovery of XML Data
• Mining of Evolving XML Data Streams
• Mining of Uncertain XML Data
• Semantics-aware Mining of XML Data
• XML Mining for Semantic Web
• XML Models for Data Mining
• XML Similarity Search and Detection