Neural networks in data mining

A. Veselý
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A. Veselý: Czech University of Agriculture, Prague, Czech Republic

Agricultural Economics, 2003, vol. 49, issue 9, 427-431

Abstract: To posses relevant information is an inevitable condition for successful enterprising in modern business. Information could be parted to data and knowledge. How to gather, store and retrieve data is studied in database theory. In the knowledge engineering, there is in the centre of interest the knowledge and methods of its formalization and gaining are studied. Knowledge could be gained from experts, specialists in the area of interest, or it can be gained by induction from sets of data. Automatic induction of knowledge from data sets, usually stored in large databases, is called data mining. Classical methods of gaining knowledge from data sets are statistical methods. In data mining, new methods besides statistical are used. These new methods have their origin in artificial intelligence. They look for unknown and unexpected relations, which can be uncovered by exploring of data in database. In the article, a utilization of modern methods of data mining is described and especially the methods based on neural networks theory are pursued. The advantages and drawbacks of applications of multiplayer feed forward neural networks and Kohonen's self-organizing maps are discussed. Kohonen's self-organizing map is the most promising neural data-mining algorithm regarding its capability to visualize high-dimensional data.

Keywords: association rules; data mining; decision trees; genetic algorithm; Kohonen's self-organizing maps; multilayered feedforward neural networks (search for similar items in EconPapers)
Date: 2003
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Persistent link: https://EconPapers.repec.org/RePEc:caa:jnlage:v:49:y:2003:i:9:id:5427-agricecon

DOI: 10.17221/5427-AGRICECON

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