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Introduction

  • In many applications we deal with huge amount of data
    • E.g. insurance company records
  • Data by itself are not useful to support decisions
    • E.g. can I make an insurance contract to this new customer or it is too risky?
  • Thus we need a set of methods to generate from a data set a “summary” that represent a conceptualization of the data set
    • E.g. what are similarities among different customers of an insurance company that divide them in different risk classes?
    • Age >25, City=Innsbruck => Low Risk
  • This is a common task that is needed in several domains to support data analysis
    • Analysis of children suffering from diabetes
    • Marketing analysis of store departments or supermarkets
  • Formal Concept Analysis is a technique that enables resolution of such problems

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