Innovations in Soft Data Analysis

  • Mika Sato-Ilic
  • Lakhmi C. Jain
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4252)


The amount of data is growing at an exponential rate. We are faced with a challenge to analyze, process and extract useful information from the vast amount of data. Traditional data analysis techniques have contributed immensely in the area of data analysis but we believe that the soft data analysis techniques, based on soft computing techniques, can be complementary and can process complicated data sets. This paper provides an introduction to the soft data analysis paradigms. It summarizes the successful and possible applications of the soft computing analysis paradigms. The merits and demerits of these paradigms are included. A number of resources available are listed and the future vision is discussed. This paper also provides a brief summary of the papers included in the session on “Innovation in Soft Data Analysis”.


Fuzzy Cluster Soft Computing Geographically Weight Regression Symbolic Data Fuzzy Regression 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Tukey, J.W.: Exploratory Data Analysis. Addison-Wesley Publishing, Reading (1977)zbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Mika Sato-Ilic
    • 1
  • Lakhmi C. Jain
    • 2
  1. 1.Faculty of Systems and Information EngineeringUniversity of TsukubaTsukubaJapan
  2. 2.Knowledge-Based Intelligent Engineering Systems CentreUniversity of South AustraliaAdelaideAustralia

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