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541Call for Papers/Special Issue of IEEE TEC on Data Mining and Knowledge Discovery with Evolutionary Algorithms

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  • Alex Alves Freitas
    Apr 1, 2002
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      (Apologies if you receive multiple copies of this CFP)


      Special Issue of IEEE Transactions on Evolutionary Computation on
      Data Mining and Knowledge Discovery with Evolutionary Algorithms

      Data mining (DM) consists of extracting interesting knowledge from
      real-world, large & complex data sets; and is the core step of a broader

      process, called the knowledge discovery from databases (KDD) process.
      In addition to the DM step, which actually extracts knowledge from
      data, the KDD process includes several preprocessing (or data
      preparation) and post-processing (or knowledge refinement) steps.
      The goal of data preprocessing methods is to transform the data
      to facilitate the application of a (or several) given DM algorithm(s),
      whereas the goal of knowledge refinement methods is to validate
      and refine discovered knowledge.
      Ideally, discovered knowledge should be not only accurate,
      but also comprehensible and interesting for the user.
      The total process is highly computation intensive.

      The idea of automatically discovering knowledge from databases
      is a very attractive and challenging task, both for academia and for
      industry. Hence, there has been a growing interest in data mining in
      several AI-related areas, including evolutionary algorithms (EAs).
      The main motivation for applying EAs to KDD tasks is that they
      are robust and adaptive search methods, which perform a
      global search in the space of candidate solutions (for instance,
      rules or another form of knowledge representation). Intuitively, the
      global search performed by EAs can more effectively discover
      interesting patterns that would have been missed by the greedy
      search performed by many KDD methods.

      The EA community has been publishing KDD-related articles
      in a relatively scattered manner in journals dedicated to knowledge
      discovery and data mining or evolutionary computing.
      The objective of this issue is to assemble a set of high-quality
      original contributions that reflect and advance the state-of-the-art
      in the area of Data Mining and Knowledge Discovery with
      Evolutionary Algorithms.
      The special issue will emphasize the utility of different evolutionary
      computing tools to various facets of KDD, ranging from theoretical
      analysis to real-life applications.

      Manuscripts should be prepared as per the format of the journal
      available at its web site:
      Submission should be made to the guest editors
      (electronic submissions in postscript or PDFare preferred)
      at ash@... or alex@....

      All submissions will be peer reviewed as per the norm of the
      IEEE Tr. on Evolutionary Computation.

      If the submission is sent by regular mail, authors are requested
      to send six copies of their manuscripts to one of the following address:

      Dr. Ashish Ghosh
      Machine Intelligence Unit
      Indian Statistical Institute
      203 B. T. Road
      Kolkata 700 108


      Dr. Alex A. Freitas
      PUCPR (Pontificia Universidade Catolica do Parana)
      PPGIA - CCET
      Rua Imaculada Conceicao, 1155
      Curitiba - PR, 80215-901

      Topics of interest include (but are not restricted to):

      Evolutionary algorithms (EAs) for data preprocessing
      (e.g., data cleaning, attribute selection, attribute
      construction), data mining (e.g., classification/prediction,
      clustering, dependence modeling, regression, extraction
      of comprehensible & interesting knowledge), or
      post-processing of extracted knowledge
      Comparison between EA based and other methods for KDD tasks
      Tailoring operators of EAs for KDD tasks
      Incorporating domain knowledge in EAs
      KDD with evolutionary intelligent agents
      Hybrid (e.g., neuro-evolutionary, rule induction-evolutionary,
      fuzzy-evolutionary) EAs for KDD
      Mining semi-structured or unstructured data (e.g., web mining,
      text mining) with EAs
      Integrating EAs with database systems
      Scaling up EAs for very large databases
      Parallel and/or distributed EAs for KDD tasks
      Application to real-life databases (e.g., biological databases,
      scientific databases, image databases)

      Papers on other topics (not listed above) related to applications
      of EAs to KDD process are also welcome.

      Important dates:

      Manuscript submission: August 31, 2002
      Notification of review reports for revision (if any): December 31, 2002

      Final version submission: February 28, 2003
      Publication of the issue: as per IEEE-TEC schedule

      Alex A. Freitas, Ph.D.
      PUCPR (Pontificia Universidade Catolica do Parana)
      PPGIA - CCET
      Rua Imaculada Conceicao, 1155
      Curitiba - PR, 80215-901