. . "2011-12-14+01:00"^^ . . "RIV/61989100:27240/11:86081141!RIV13-GA0-27240___" . . "1865-0929" . . . . . . "Parallel hybrid SOM learning on high dimensional sparse data"@en . "Springer-Verlag" . "Computer Information Systems - Analysis and Technologies international conference, CISIM 2011 : proceedings" . "P(GA205/09/1079), S" . . . "4"^^ . "219505" . "Dvorsk\u00FD, Ji\u0159\u00ED" . "10.1007/978-3-642-27245-5_29" . . . . "Parallel hybrid SOM learning on high dimensional sparse data" . "Martinovi\u010D, Jan" . "RIV/61989100:27240/11:86081141" . . . . . . . "978-3-642-27244-8" . "5"^^ . "[44E4F5140F4E]" . "Self organizing maps (also called Kohonen maps) are known for their capability of projecting high-dimensional space into lower dimensions. There are commonly discussed problems like rapidly increased computational complexity or specific similarity representation in the high-dimensional space. In the paper there is proposed the effective clustering algorithm based on self organizing map with the main purpose to reduce high dimension of the input dataset. The problem of computational complexity is solved using parallelization; the speed of proposed algorithm is accelerated using the algorithm version suitable for data collections with certain level of sparsity."@en . "Voj\u00E1\u010Dek, Luk\u00E1\u0161" . . "Parallel hybrid SOM learning on high dimensional sparse data"@en . . . "Sparse data; Similarity representation; Parallelizations; High-dimensional; High dimensions; High dimensional spaces; Data sets; Data collection"@en . "Dordrecht" . "Slaninov\u00E1, Kate\u0159ina" . "27240" . "8"^^ . "Parallel hybrid SOM learning on high dimensional sparse data" . . "Self organizing maps (also called Kohonen maps) are known for their capability of projecting high-dimensional space into lower dimensions. There are commonly discussed problems like rapidly increased computational complexity or specific similarity representation in the high-dimensional space. In the paper there is proposed the effective clustering algorithm based on self organizing map with the main purpose to reduce high dimension of the input dataset. The problem of computational complexity is solved using parallelization; the speed of proposed algorithm is accelerated using the algorithm version suitable for data collections with certain level of sparsity." . . . "Kalkata" . "Vondr\u00E1k, Ivo" .