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Reducing the number of paths in a minimized project-network with given bounds on the durations of activities

Viattchenin D.

Operations Research and Decisions

2015

Vol. 25, No. 4

71--87

This paper deals in a preliminary way with the problem of selecting the smallest possible number of dominant paths in a minimized project-network with given bounds on the permissible values of the durations of activities. For this purpose, a classification technique is proposed. This technique is based on a heuristic possibilistic clustering of interval-valued data. The basic concepts of heuristic possibilistic clustering are defined and methods for preprocessing interval-valued data are described. An illustrative example is considered in detail and some conclusions are formulated.

A new heuristic possibilistic clustering algorithm for feature selection

Kacprzyk J., Owsinski J. W., Viattchenin D. A.

Journal of Automation Mobile Robotics and Intelligent Systems

2014

Vol. 8, No. 2

40--46

The paper deals with the problem of selection of the most informative features. A new effective and efficient heuristic possibilistic clustering algorithm for feature selection is proposed. First, a brief description of basic concepts of the heuristic approach to possibilistic clustering is provided. A technique of initial data preprocessing is described and a fuzzy correlation measure is considered. The new algorithm is described and then illustrated on the well-known Iris data set benchmark and the results obtained are compared with those by using the conventional, well-known and widely employed method of principal component analysis (PCA). Conclusions and suggestions for future research are given.