Cluster_KDD: A Visual Clustering and Knowledge Discovery Platform Based on Concept Lattice

  • Amel Grissa Touzi
  • Amira Aloui
  • Rim Mahouachi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7332)


Nowadays, with the evolution of the data in data processing and storage of great volumes of these diversified data, the software of Data Mining became without context a necessity for the majority of the users of the Information Systems. Unfortunately, currently marketed software are very limited and don’t meet all user needs. This software supports only some classification algorithms and some Knowledge Discovery in Databases (KDD) algorithms that generate a big number of rules which are not understandable by the end user. Moreover, these approaches are applicable only for restricted data type. In this paper, we propose new software of classification and KDD, called Cluster-KDD, which supports a larger set of data type and classification algorithm and offers KDD algorithms that generate comprehensible and exploitable rules by the user.


Data mining Knowledge Discovery in Databases Clustering Formal Concept Analysis Fuzzy Logic 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Amel Grissa Touzi
    • 1
  • Amira Aloui
    • 1
  • Rim Mahouachi
    • 1
  1. 1.Ecole Nationale d’Ingénieurs de TunisTunisTunisia

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