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: Advances in Knowledge Discovery and Data Mining
    sub title: 6th Pacific-Asia Conference, PAKDD 2002, Taipei, Taiwan, May 6-8, 2002. Proceedings
  name:
    name part: Cheng, Ming-Syan.
    role:
      role term: editor.
    role:
      role term: edt
    role:
      role term: http://id.loc.gov/vocabulary/relators/edt
  name:
    name part: Yu, Philip S.
    role:
      role term: editor.
    role:
      role term: edt
    role:
      role term: http://id.loc.gov/vocabulary/relators/edt
  name:
    name part: Liu, Bing.
    role:
      role term: editor.
    role:
      role term: edt
    role:
      role term: http://id.loc.gov/vocabulary/relators/edt
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: gw
    date issued: 2002
    edition: 1st ed. 2002.
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: XIV, 570 p. online resource.
  abstract: Knowledge discovery and data mining have become areas of growing significance because of the recent increasing demand for KDD techniques, including those used in machine learning, databases, statistics, knowledge acquisition, data visualization, and high performance computing. In view of this, and following the success of the five previous PAKDD conferences, the sixth Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2002) aimed to provide a forum for the sharing of original research results, innovative ideas, state-of-the-art developments, and implementation experiences in knowledge discovery and data mining among researchers in academic and industrial organizations. Much work went into preparing a program of high quality. We received 128 submissions. Every paper was reviewed by 3 program committee members, and 32 were selected as regular papers and 20 were selected as short papers, representing a 25% acceptance rate for regular papers. The PAKDD 2002 program was further enhanced by two keynote speeches, delivered by Vipin Kumar from the Univ. of Minnesota and Rajeev Rastogi from AT&T. In addition, PAKDD 2002 was complemented by three tutorials, XML and data mining (by Kyuseok Shim and Surajit Chadhuri), mining customer data across various customer touchpoints at- commerce sites (by Jaideep Srivastava), and data clustering analysis, from simple groupings to scalable clustering with constraints (by Osmar Zaiane and Andrew Foss).
  table of contents: Industrial Papers (Invited) -- Network Data Mining and Analysis: The Project -- Privacy Preserving Data Mining: Challenges and Opportunities -- Survey Papers (Invited) -- A Case for Analytical Customer Relationship Management -- On Data Clustering Analysis: Scalability, Constraints, and Validation -- Association Rules (I) -- Discovering Numeric Association Rules via Evolutionary Algorithm -- Efficient Rule Retrieval and Postponed Restrict Operations for Association Rule Mining -- Association Rule Mining on Remotely Sensed Images Using P-trees -- On the Efficiency of Association-Rule Mining Algorithms -- Classification (I) -- A Function-Based Classifier Learning Scheme Using Genetic Programming -- SNNB: A Selective Neighborhood Based Naïve Bayes for Lazy Learning -- A Method to Boost Naïve Bayesian Classifiers -- Toward Bayesian Classifiers with Accurate Probabilities -- Interestingness -- Pruning Redundant Association Rules Using Maximum Entropy Principle -- A Confidence-Lift Support Specification for Interesting Associations Mining -- Concise Representation of Frequent Patterns Based on Generalized Disjunction-Free Generators -- Mining Interesting Association Rules: A Data Mining Language -- The Lorenz Dominance Order as a Measure of Interestingness in KDD -- Sequence Mining -- Efficient Algorithms for Incremental Update of Frequent Sequences -- DELISP: Efficient Discovery of Generalized Sequential Patterns by Delimited Pattern-Growth Technology -- Self-Similarity for Data Mining and Predictive Modeling A Case Study for Network Data -- A New Mechanism of Mining Network Behavior -- Clustering -- M-FastMap: A Modified FastMap Algorithm for Visual Cluster Validation in Data Mining -- An Incremental Hierarchical Data Clustering Algorithm Based on Gravity Theory -- Adding Personality to Information Clustering -- Clustering Large Categorical Data -- Web Mining -- WebFrame: In Pursuit of Computationally and Cognitively Efficient Web Mining -- Naviz:Website Navigational …
  note: edited by Ming-Syan Cheng, Philip S. Yu, Bing Liu.
  subject:
    topic: Artificial intelligence
  subject:
    topic: Data structures (Computer science)
  subject:
    topic: Database management
  subject:
    topic: Mathematical statistics
  subject:
    topic: Statistics
  subject:
    topic: Computers and civilization
  subject:
    topic: Artificial Intelligence
  subject:
    topic: Data Structures and Information Theory
  subject:
    topic: Database Management
  subject:
    topic: Probability and Statistics in Computer Science
  subject:
    topic: Statistics and Computing/Statistics Programs
  subject:
    topic: Computers and Society
  classification: Q334-342
  classification: 006.3
  related item:
    title info:
      title: Springer eBooks
  related item:
    title info:
      title: Lecture Notes in Artificial Intelligence ; 2336
  identifier: 9783540478874
  identifier: https://doi.org/10.1007/3-540-47887-6
  record info:
    record creation date: 121227
    record change date: 20191022053138.0
    record identifier: 978-3-540-47887-4