350 rub
Journal Science Intensive Technologies №12 for 2015 г.
Article in number:
The approach to identifying non-stationary states of complex objects
Authors:
A.A. Elshin, A.V. Elshin
Abstract:
One of the features of modern management approaches is to improve the adequacy of the models. Wwith a special interest for modeling of present organizational and ergatic in behavior non-stationary models which occur non-stationary state. The article considers approaches to identifying such conditions. As a result of their analysis of a selected mathematical tools of Association rules is selected. It is shown that in a limited period, the cumulative effect on the system, informative of thewith respect to associations as decision rules, can be improved by the formation of rules within the specified period of exposure. It is experimentally proved that the proposed approach is effective with a small number of events that form antecedent, and small dimension of the alphabet of input actions.
Pages: 56-61
References

 

  1. Zagorujjko N.G. Prikladnye metody analiza dannykh i znanijj. Novosibirsk: IM SO RAN. 1999. 270 s.
  2. Gorodeckijj V.I., Tushkanova O.N. Associativnaja klassifikacija: analiticheskijj obzor. CH. 1. / Trudy SPIIRAN. 2015. Vyp. 1(38). S. 183-203.
  3. Agrawal R., Sricant R. Fast Algorithm for Mining Association rules // Proceedings of the 20th Intern. ConferenceonVeryLargeDatabases. Santiago. Chile. 1994. R. 68-77.
  4. Piatetsky-Shapiro G.Discover, analysis, and presentation of strong rules // Knowledge discovery from Databases. G. Piatetsky-Shapiro and W.Frawley (Eds.). AAAI Press/MIT Press. 1991. R. 229-248.
  5. Ghaderi R., Minaei-Bidgoli B. Detecting Data Errors with Employing Negative Association Rules // International Journal of Digital Content Technology and its Applications V. 3. Sept. 2009. № 3. R. 91-95.
  6. KazienkoP. Mining Indirect Association Rules for Web Recommendations //Applied Math Computer Science. 2009. V. 19. № 1. R. 165-186.
  7. Agrawal R., Imieliński T., Swami A. Mining association rules between sets of items in large databases // In Proc. of  the 1993 ACM SIGMOD international conference on Management of data (SIGMOD \'93). p. 207-216.
  8. Dong G., Li J. Efficient Mining of Emerging Patterns: Discovering Trendsand Differences // Proc. of the KDD\'99. 1999. R. 43-52.