Parallel Computing for K-means Clustering on Estimating Underlying Latent Classes
碩士 === 國立交通大學 === 統計學研究所 === 98 === The main purpose of the study is to perform parallel computing for k-means clustering on estimating the underlying latent class process. OpenMP and MPI parallel computing make computing time shorter for updated and non-updated k-means clustering method. We compare...
| Main Authors: | , |
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| Other Authors: | |
| Format: | Others |
| Language: | zh-TW |
| Published: |
2010
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| Online Access: | http://ndltd.ncl.edu.tw/handle/67969272955078091677 |