@misc{Dziedzic_Mateusz_New_2012,
 author={Dziedzic, Mateusz},
 copyright={Creative Commons Attribution BY 4.0 license},
 address={Warszawa},
 journal={Książka = Book},
 howpublished={online},
 year={2012},
 publisher={Instytut Badań Systemowych. Polska Akademia Nauk},
 publisher={Systems Research Institute. Polish Academy of Sciences},
 language={eng},
 abstract={Computer aided data mining, and in particular methods of clustering, its widely used technique, develop very rapidly nowadays. Basically, clustering is an unsupervised learning technique. However, there is a growing interest in considering the case where there is a partial knowledge on the actual grouping of the objects available. This knowledge may take form of the hints on the co-occurrence of object in the same clusters. In this paper we propose to solve this problem of constrained clustering using the technique of differential evolution (DE). We show the efficiency and usefulness of differential evolution in hard- and soft-constrained clustering tasks. Some practical examples of the clustering problems are examined and results obtained are compared to two variants of a classic clustering technique, the k-means algorithm.},
 type={Text},
 title={New developments in fuzzy sets, intuitionistic fuzzy sets, generalized nets and related topics. Volume II: applications * Differential evolution in clustering with constraints},
 URL={http://rcin.org.pl/Content/205934/PDF/KS-2012-04-P05.pdf},
}