"Combined method for effective clustering based on parallel SOM and spectral clustering" . . "Combined method for effective clustering based on parallel SOM and spectral clustering" . . "DATESO 2011 : databases, texts, specifications, and objects : proceedings of the Dateso 2011 Workshop" . . . "Slaninov\u00E1, Kate\u0159ina" . . "978-80-248-2391-1" . . . "12"^^ . . . "5"^^ . "Vysok\u00E1 \u0161kola b\u00E1\u0148sk\u00E1 - Technick\u00E1 univerzita, Fakulta elektrotechniky a informatiky, Katedra informatiky" . . "[3FCFC628DF6F]" . "The paper is oriented to the problem of clustering for large datasets with high-dimensions. We propose a two-phase combined method with regard to high dimensions and exploiting the standard clustering algorithm. The first step of the method is based on the learning phase using artificial neural network, especially Self organizing map, which we find as a suitable method for the reduction of the problem complexity. Due to the fact, that the learning phase of artificial neural networks can be time-consuming operation (especially for large highdimensional datasets), we decided to accelerate this phase using parallelization to improve the computational efficiency. The second phase of the proposed method is oriented to clustering. Because the visualization provided by Self organizing maps is depending on the map dimension, and is not as clear and comprehensible in the cases of clustering applications, we decided to use spectral clustering algorithm to obtain sufficient clusters. According to our results, the proposed combined method is sufficiently rapid and quite accurate." . "The paper is oriented to the problem of clustering for large datasets with high-dimensions. We propose a two-phase combined method with regard to high dimensions and exploiting the standard clustering algorithm. The first step of the method is based on the learning phase using artificial neural network, especially Self organizing map, which we find as a suitable method for the reduction of the problem complexity. Due to the fact, that the learning phase of artificial neural networks can be time-consuming operation (especially for large highdimensional datasets), we decided to accelerate this phase using parallelization to improve the computational efficiency. The second phase of the proposed method is oriented to clustering. Because the visualization provided by Self organizing maps is depending on the map dimension, and is not as clear and comprehensible in the cases of clustering applications, we decided to use spectral clustering algorithm to obtain sufficient clusters. According to our results, the proposed combined method is sufficiently rapid and quite accurate."@en . . "Combined method for effective clustering based on parallel SOM and spectral clustering"@en . "Spectral clustering; Second phase; Problem complexity; Parallelizations; Learning phase; Large datasets; High-dimensional; High dimensions; Data sets; Combined method; Clustering applications"@en . "Dr\u00E1\u017Edilov\u00E1, Pavla" . "5"^^ . . . "190890" . . . "Dvorsk\u00FD, Ji\u0159\u00ED" . . "RIV/61989100:27240/11:86084592" . . . "27240" . "Voj\u00E1\u010Dek, Luk\u00E1\u0161" . . "RIV/61989100:27240/11:86084592!RIV13-GA0-27240___" . . . . . . "2011-04-20+02:00"^^ . "P\u00EDsek" . . "P(GA205/09/1079)" . . "Ostrava" . "Martinovi\u010D, Jan" . "Combined method for effective clustering based on parallel SOM and spectral clustering"@en . . . .