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- W2890903893 abstract "The size, complexity and dimensionality of data collections are ever increasing from the beginning of the computer era. Clustering methods, such as Growing Neural Gas (GNG) [10] that is based on unsupervised learning, is used to reveal structures and to reduce large amounts of raw data. The growth of computational complexity of such clustering method, caused by growing data dimensionality and the specific similarity measurement in a high-dimensional space, reduces the effectiveness of clustering method in many real applications. The growth of computational complexity can be partially solved using the parallel computation facilities, such as High Performance Computing (HPC) cluster with MPI. An effective parallel implementation of GNG is discussed in this paper, while the main focus is on minimizing of interprocess communication which depends on the number of neurons and edges among neurons in the neural network. A new algorithm of adding neurons depending on data density is proposed in the paper." @default.
- W2890903893 created "2018-09-27" @default.
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- W2890903893 date "2018-01-01" @default.
- W2890903893 modified "2023-09-27" @default.
- W2890903893 title "Growing Neural Gas Based on Data Density" @default.
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- W2890903893 doi "https://doi.org/10.1007/978-3-319-99954-8_27" @default.
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