PROACTIVE DECISION-MAKING MECHANISM BASED ON MINING TECHNOLOGY
Galyna O. Chornous
Published 2012-01-01
https://doi.org/10.15388/Ekon.2012.0.904
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How to Cite

Chornous G. O. (2012) “PROACTIVE DECISION-MAKING MECHANISM BASED ON MINING TECHNOLOGY”, Ekonomika, 91(1), pp. 105-117. doi: 10.15388/Ekon.2012.0.904.

Abstract

The main idea of this study is to connect the possibilities of mining technology with the methodology of proactive management by social and economic systems. The permanent process of complication of all spheres of social life requires improving the management forms and methods. Modern methods of decision support, appropriate information technology make it possible to improve the classical approaches, one of which is proactive management. Taking into account the limits of classical methods, proactive management should be chosen as an appropriate mining technology that can automatically extract the new non-trivial knowledge from data in the form of patterns, relationships, laws, etc. This synthetic technology combines the latest achievements of artificial intelligence, mathematics, statistics, heuristic approaches, including Data Mining, OLAP and others. Using the mining technology enables: to implement data monitoring, preparation and analysis (collection and presentation of data, detection of situations), to identify problem situations (to recognize patterns of problem situations; to correlate the pattern of the current situation with patterns of problem situations; to determine the structure of the problem situation, to identify factors and relationships), to prioritize the problems, trends and challenges, their expectations, effects (to predict the situation development with managerial influence and without it), to pose the tasks (to analyze deviations in terms of activity; to define goals, criteria, operating conditions) and so on. The following models (using the methods of “nearest neighbour”, rules induction, causal networks, statistical methods, associations, neural networks, decision trees, etc.) can be used: cluster allocation situations, classification of patterns, models of situation identification, pattern recognition models, prediction models, optimization models, causal relationships models.

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