XML Data Mining: Models, Methods, and Applications, 1st Edition

  • Andrea Tagarelli
  • Published By:
  • ISBN-10: 1613503571
  • ISBN-13: 9781613503577
  • DDC: 006.3
  • Grade Level Range: College Freshman - College Senior
  • 538 Pages | eBook
  • Original Copyright 2012 | Published/Released November 2012
  • This publication's content originally published in print form: 2012

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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.

Table of Contents

Front Cover.
Title Page.
Copyright Page.
Editorial Advisory Board.
List of Reviewers.
Table of Contents.
1: Models and Measures.
2: A Study of XML Models for Data Mining: Representations, Methods, and Issues.
3: Modeling, Querying, and Mining Uncertain XML Data.
4: XML Similarity Detection and Measures.
5: Efficient Identification of Similar XML Fragments Based on Tree Edit Distance.
6: Clustering and Classification.
7: Approximate Matching Between XML Documents and Schemas with Applications in XML Classification and Clustering.
8: The Role of Schema and Document Matchings in XML Source Clustering.
9: XML Document Clustering: An Algorithmic Perspective.
10: Fuzzy Approaches to Clustering XML Structures.
11: XML Tree Classification on Evolving Data Streams.
12: Data Driven Encoding of Structures and Link Predictions in Large XML Document Collections.
13: Association Mining.
14: Frequent Pattern Discovery and Association Rule Mining of XML Data.
15: A Framework for Mining and Querying Summarized XML Data through Tree-Based Association Rules.
16: Discovering Higher Level Correlations from XML Data.
17: Semantics-Aware Mining.
18: XML Mining for Semantic Web.
19: A Component-Based Framework for the Integration and Exploration of XML Sources.
20: Matching XML Documents at Structural and Conceptual Level Using Subtree Patterns.
21: Applications.
22: Geographical Map Annotation with Significant Tags Available from Social Networks.
23: Organizing XML Documents on a Peer–to–Peer Network by Collaborative Clustering.
24: Incorporating Qualitative Information for Credit Risk Assessment through Frequent Subtree Mining for XML.
About the Contributors.