| 書(shū)目名稱(chēng) | Spatial AutoRegression (SAR) Model |
| 副標(biāo)題 | Parameter Estimation |
| 編輯 | Baris M. Kazar,Mete Celik |
| 視頻video | http://file.papertrans.cn/874/873431/873431.mp4 |
| 叢書(shū)名稱(chēng) | SpringerBriefs in Computer Science |
| 圖書(shū)封面 |  |
| 描述 | Explosive growth in the size of spatial databases has highlighted the need for spatial data mining techniques to mine the interesting but implicit spatial patterns within these large databases.This book explores computational structure of the exact and approximate spatialautoregression (SAR) model solutions. Estimation of the parameters of the SAR model using Maximum Likelihood (ML) theory is computationally very expensive because of the need to compute the logarithm of the determinant (log-det) of a large matrix in the log-likelihood function.The second part of the book introduces theory on SAR model solutions. The third part of the book applies parallel processing techniques to the exact SAR model solutions. Parallel formulations of the SAR model parameter estimation procedure based on ML theory are probed using data parallelism with load-balancing techniques.Although this parallel implementation showed scalability up to eight processors, the exact SAR model solution still suffers from high computational complexity and memory requirements. These limitations have led the book to investigate serial and parallel approximate solutions for SAR model parameter estimation. In the fourth |
| 出版日期 | Book 2012 |
| 關(guān)鍵詞 | Maximum Likelihood Theory; Spatial Autocorrelation; Spatial Autoregression Model; Spatial Data Mining; S |
| 版次 | 1 |
| doi | https://doi.org/10.1007/978-1-4614-1842-9 |
| isbn_softcover | 978-1-4614-1841-2 |
| isbn_ebook | 978-1-4614-1842-9Series ISSN 2191-5768 Series E-ISSN 2191-5776 |
| issn_series | 2191-5768 |
| copyright | The Author(s) 2012 |
|Archiver|手機(jī)版|小黑屋|
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