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Auer Peter Dósa György Dulai Tibor Fügenschuh Armin Näser Peggy Ortner Ronald Werner-Stark Ágnes 《Central European Journal of Operations Research》2021,29(3):1079-1113
Central European Journal of Operations Research - We deal with a very complex and hard scheduling problem. Two types of products are processed by a heterogeneous resource set, where resources have... 相似文献
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Agnes Vathy-Fogarassy Agnes Werner-Stark Janos Abonyi 《Journal of Mathematical Modelling and Algorithms》2008,7(4):351-370
In practical data mining tasks, high-dimensional data has to be analyzed. In most of the cases it is very informative to map
and visualize the hidden structure of a complex data set in a low-dimensional space. In this paper a new class of mapping
algorithms is defined. These algorithms combine topology representing networks and different nonlinear mapping algorithms.
While the former methods aim to quantify the data and disclose the real structure of the objects, the nonlinear mapping algorithms
are able to visualize the quantized data in the low-dimensional vector space. In this paper, techniques based on these methods
are gathered and the results of a detailed analysis performed on them are shown. The primary aim of this analysis is to examine
the preservation of distances and neighborhood relations of the objects. Preservation of neighborhood relations was analyzed
both in local and global environments. To evaluate the main properties of the examined methods we show the outcome of the
analysis based both on synthetic and real benchmark examples. 相似文献
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