04125ntm a22005537i 4500 000310392 CZ-PrVSE 20160917102307.0 m d cr n|||||||||| 160917s2016 xr fsbm 000 0 eng d NEZPRACOVANÝ IMPORT ABA006 cze ABA006 ABA006 rda Říhová, Elena ISIS:103170 dis Hodnocení Výsledků Fuzzy Shlukování eng Evaluating of Fuzzy Clustering Results / Elena Říhová 2016 ?? s. : digital, PDF soubor Vedoucí práce: Iva Pecáková Disertační práce (Ph.D.)—Vysoká škola ekonomická v Praze. Fakulta informatiky a statistiky, 2016 Obsahuje bibliografii Textový (vysokoškolská kvalifikační práce) Rok obhajoby 2016 Cluster analysis is a multivariate statistical classification method, implying different methods and procedures. Clustering methods can be divided into hard and fuzzy; the latter one provides a more precise picture of the information by clustering objects than hard clustering. But in practice, the optimal number of clusters is not known a priori, and therefore it is necessary to determine the optimal number of clusters. To solve this problem, the validity indices help us. However, there are many different validity indices to choose from. One of the goals of this work is to create a structured overview of existing validity indices and techniques for evaluating fuzzy clustering results in order to find the optimal number of clusters. The main aim was to propose a new index for evaluating the fuzzy clustering results, especially in cases with a large number of clusters (defined as more than five). The newly designed coefficient is based on the degrees of membership and on the distance (Euclidean distance) between the objects, i.e. based on principles from both fuzzy and hard clustering. The suitability of selected validity indices was applied on real and generated data sets with known optimal number of clusters a priory. These data sets have different sizes, different numbers of variables, and different numbers of clusters. The aim of the current work is regarded as fulfilled. A key contribution of this work was a new coefficient (E), which is appropriate for evaluating situations with both large and small numbers of clusters. Because the new validity index is based on the principles of both fuzzy clustering and hard clustering, it is able to correctly determine the optimal number of clusters on both small and large data sets. A second contribution of this research was a structured overview of existing validity indices and techniques for evaluating the fuzzy clustering results. Způsob přístupu: Internet statistika [obor disert. práce] disertace fd132024 czenas dissertations eczenas evaluating fuzzy clustering results validity indices fuzzy sets fuzzy clustering Pecáková, Iva, 1955- jo2003197032 ths Řezanková, Hana opn Vysoká škola ekonomická v Praze. Fakulta informatiky a statistiky kn20010709399 dgg https://insis.vse.cz/zp/42026/podrobnosti VŠKP v InSIS https://insis.vse.cz/zp/42026 Hlavní práce https://insis.vse.cz/zp/42026/posudek/vedouci Hodnocení vedoucího https://insis.vse.cz/zp/42026/posudek/oponent/48884 Oponentura https://insis.vse.cz/zp/42026/posudek/oponent/48885 Oponentura https://insis.vse.cz/zp/42026/priloha/14006 Přiloha k práci https://insis.vse.cz/zp/42026/priloha/14007 Přiloha k práci https://insis.vse.cz/zp/42026/priloha/14008 Přiloha k práci https://insis.vse.cz/zp/42026/podrobnosti dc:identifier NEPOSILAT VSKP vse42026 160908 42026