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Knowledge discovery with clustering based on rules. Interpreting results

  • Karina Gibert
  • Tomàs Aluja
  • Ulises Cortés
Communications Session 4. Clustering and Discretization
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1510)

Abstract

It is clear that nowadays analysis of complex systems is an important handicap in Statistics, Artificial Intelligence, Information Systems, Data visualization, and other fields.

Describing the structure or obtaining knowledge of complex systems is known as a difficult task. The combination of Data Analysis techniques (including clustering), Inductive Learning (knowledge-based systems), Management of Data Bases and Multidimensional Graphical Representation must produce benefits on this field.

Clustering based on rules (CBR) is a methodology developed with the aim of finding the structure of complex domains, which performs better than traditional clustering algorithms or knowledge based systems approaches. In our proposal, a combination of clustering and inductive learning is focussed to the problem of finding and interpreting special patterns (or concepts) from large data bases, in order to extract useful knowledge to represent real-world domains. This methodology and its behaviour as a Knowledge Discovery has been, in fact, presented in previous papers ([3],

The aim of this paper is to emphasize the reporting phase. Some tools oriented to the interpretation of the clusters are presented; automatic rules generation is presented and applied to a real research. Actually, in a KD system, data preparation and interpretation of the results is as important as the analysis itself. In this paper, missing data treatment is analysed; a statistical test, based on non parametric techniques, for comparing several classifications is presented. Also, a method for finding characteristic values of the classes is presented; this is based on the prototype of each class. Finally, these characterizations allow automatic generation of decision rules, as a predictive tool for future items.

Keywords

Combining many methods in one system statistical tests in KDD applications medicine: diagnosis and prognosis from concept learning to concept discovery Prior domain knowledge and use of discovered knowledge 

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Copyright information

© Springer-Verlag Berlin Heidelberg 1998

Authors and Affiliations

  • Karina Gibert
    • 1
  • Tomàs Aluja
    • 1
  • Ulises Cortés
    • 2
  1. 1.Dep. of Statistics and Operation ResearchUniversitat Politècnica de Catalunya5. BarcelonaSpain
  2. 2.Department of SoftwareUniversitat Politècnica de Catalunya5. BarcelonaSpain

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