Abstract
This chapter presents a new algorithm called “Population” that is an efficient and high speed method of performing multi dimensional scaling based only on the calculation of a local fitness. Population is not bound to a specific cost function; it is possible to define itself by means of its relation to a considered objective. The motivation for its creation was for use in the elaboration of datasets of great dimension. In performance comparisons between Population and the Sammon method, Population has consistently excelled. Because of the nature of the algorithm, it is not necessary for the data set to be complete at the moment of the elaboration, for new data can be dynamically introduced in the system.
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Massini, G., Terzi, S., Buscema, M. (2013). Population Algorithm: A New Method of Multi-Dimensional Scaling. In: Tastle, W. (eds) Data Mining Applications Using Artificial Adaptive Systems. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-4223-3_3
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DOI: https://doi.org/10.1007/978-1-4614-4223-3_3
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