Data, Text, and Web Mining for Business Analytics Minitrack

Data mining is the process of discovering valid, novel, potentially useful, and ultimately understandable patterns (i.e., nuggets of knowledge) in data stored in structured databases, where the data is organized in records populated by categorical, ordinal and continuous variables. Text mining, on the other hand, refers to the very same discovery process as it applies to unstructured data sources including business documents, customer comments, Web pages, and XML files.

This minitrack focuses on decision support aspects of advanced analytics, with emphasis on data, text and Web mining. Topic areas covered in this minitrack include, but are not limited to:

  • New methods and algorithms of data/text/Web mining
  • New and improved processes and methodologies of conducting data/text/Web mining
  • Data acquisition, integration and pre-processing related research topics of data/text/Web mining, such as new and novel ways of data integration/transformation/characterization, data cleaning/scrubbing, data sampling, data reduction, data visualization, etc.
  • Novel, interesting and impactful applications of data/text/Web mining for better managerial decision making
  • Ethical and privacy issues in data/text/Web mining
  • Futuristic directions for data/text/Web mining in the era of Big Data analytics

Accepted papers will be considered for fast-tracking into a special issue on Data Mining & Decision Analytics for Decision Sciences journal. This special issue welcomes the best submissions of HICSS-50. Participants of this minitrack are highly encouraged to submit their extended and enriched papers to this special issue, fully conforming to the specifications of the Decision Sciences journal as outlined by its Author Guidelines. See CFP for additional information.

Minitrack Co-chairs:

Dursun Delen (Primary Contact)
Oklahoma State University

Enes Eryarsoy
Sehir University, Turkey

Şadi E. Şeker

Istanbul Medeniyet University

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