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Computational intelligence, soft computing, data mining: introduction

   

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A numner of terms in the title compete to name the same interdisciplinary area. It is difficult, if not impossible, to accommodate in a formal definition disparate areas with their own established individualities such as fuzzy sets, neural networks, evolutionary computation, machine learning, Bayesian reasoning, etc.

Following a good academic tradition, an individual or a group of researchers often identifies an area which is slightly different from an already existing one, introduces terminology, organises a new conference, a journal, professorship positions, school of thought, etc. This is what was happening with areas close to artificial intelligence (AI) during the last two decades. Data mining (DM), knowledge discovery in databases (KDD), computational intelligence (CI), machine learning (ML), intelligent data analysis (IDA), soft computing, pattern recognition - all these areas very much intersecting, with a similar focus and application areas. It is really difficult to find a clear-cut difference between them. Still certain differences can be formulated:

· computational intelligence (CI) is seen as "a new name" for a group of techniques attributed earlier largely to AI: neural networks, fuzzy systems, and genetic and evolutionary computation;
· machine learning (ML) is an area of computer science, a sub-area of AI concentrating on the theoretical foundations. Classification (pattern recognition) problems are addressed by ML more often than regression (numerical prediction) problems. Technically speaking, most of ML problems can be formulated as problems of function approximation.
· data mining (DM) and knowledge discovery in databases (KDD) are focused often at very large databases and are associated with applications in banking, financial services and customer resources management (CRM). DM is seen as a part of a wider KDD. Methods used are mainly from statistics and ML.
· intelligent data analysis (IDA) is relatively new and seem to concentrate more on the data analysis in medicine and research. Methods used are also from statistics and ML;
· soft computing, and in particular fuzzy rule-base systems induced from data.

There is a number of methods that appeared to be very useful and are claimed by all fields mentioned above, and which are covered on this site. These are artificial neural networks, fuzzy logic and global and evolutionary optimization. The methods of non-linear dynamics (chaos theory) are lately also seen as belonging to the mentioned areas.