rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Data Mining: Foundations and Practice
  name:
    name part: Lin, Tsau Young.
    role:
      role term: editor.
  name:
    name part: Xie, Ying.
    role:
      role term: editor.
  name:
    name part: Wasilewska, Anita.
    role:
      role term: editor.
  name:
    name part: Liau, Churn-Jung.
    role:
      role term: editor.
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: gw
    date issued: 2008
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: XVI, 562 p. 129 illus., 25 illus. in color. online resource.
  abstract: This book contains valuable studies in data mining from both foundational and practical perspectives. The foundational studies of data mining may help to lay a solid foundation for data mining as a scientific discipline, while the practical studies of data mining may lead to new data mining paradigms and algorithms. The foundational studies contained in this book focus on a broad range of subjects, including conceptual framework of data mining, data preprocessing and data mining as generalization, probability theory perspective on fuzzy systems, rough set methodology on missing values, inexact multiple-grained causal complexes, complexity of the privacy problem, logical framework for template creation and information extraction, classes of association rules, pseudo statistical independence in a contingency table, and role of sample size and determinants in granularity of contingency matrix. The practical studies contained in this book cover different fields of data mining, including rule mining, classification, clustering, text mining, Web mining, data stream mining, time series analysis, privacy preservation mining, fuzzy data mining, ensemble approaches, and kernel based approaches. We believe that the works presented in this book will encourage the study of data mining as a scientific field and spark collaboration among researchers and practitioners.
  table of contents: Compact Representations of Sequential Classification Rules -- An Algorithm for Mining Weighted Dense Maximal 1-Complete Regions -- Mining Linguistic Trends from Time Series -- Latent Semantic Space for Web Clustering -- A Logical Framework for Template Creation and Information Extraction -- A Bipolar Interpretation of Fuzzy Decision Trees -- A Probability Theory Perspective on the Zadeh Fuzzy System -- Three Approaches to Missing Attribute Values: A Rough Set Perspective -- MLEM2 Rule Induction Algorithms: With and Without Merging Intervals -- Towards a Methodology for Data Mining Project Development: The Importance of Abstraction -- Fining Active Membership Functions in Fuzzy Data Mining -- A Compressed Vertical Binary Algorithm for Mining Frequent Patterns -- Naïve Rules Do Not Consider Underlying Causality -- Inexact Multiple-Grained Causal Complexes -- Does Relevance Matter to Data Mining Research? -- E-Action Rules -- Mining E-Action Rules, System DEAR -- Definability of Association Rules and Tables of Critical Frequencies -- Classes of Association Rules: An Overview -- Knowledge Extraction from Microarray Datasets Using Combined Multiple Models to Predict Leukemia Types -- On the Complexity of the Privacy Problem in Databases -- Ensembles of Least Squares Classifiers with Randomized Kernels -- On Pseudo-Statistical Independence in a Contingency Table -- Role of Sample Size and Determinants in Granularity of Contingency Matrix -- Generating Concept Hierarchies from User Queries -- Mining Efficiently Significant Classification Association Rules -- Data Preprocessing and Data Mining as Generalization -- Capturing Concepts and Detecting Concept-Drift from Potential Unbounded, Ever-Evolving and High-Dimensional Data Streams -- A Conceptual Framework of Data Mining -- How to Prevent Private Data from being Disclosed to a Malicious Attacker -- Privacy-Preserving Naive Bayesian Classification over Horizontally Partitioned Data -- Using Association Rules for Classifica…
  note: edited by Tsau Young Lin, Ying Xie, Anita Wasilewska, Churn-Jung Liau.
  subject:
    topic: Engineering
  subject:
    topic: Artificial intelligence
  subject:
    topic: Applied mathematics
  subject:
    topic: Engineering mathematics
  subject:
    topic: Engineering
  subject:
    topic: Appl.Mathematics/Computational Methods of Engineering
  subject:
    topic: Artificial Intelligence (incl. Robotics)
  classification: TA329-348
  classification: TA640-643
  classification: 519
  related item:
    title info:
      title: Springer eBooks
  related item:
    title info:
      title: Studies in Computational Intelligence, 118
  identifier: 9783540784883
  identifier: http://dx.doi.org/10.1007/978-3-540-78488-3
  record info:
    record creation date: 100301
    record change date: 20151103141042.0
    record identifier: 978-3-540-78488-3