| Autor: |
Simon Dexter, Gavriel Yarmish, Philip Listowsky |
| Rok vydání: |
2017 |
| Předmět: |
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| Zdroj: |
2017 International Conference on Computational Science and Computational Intelligence (CSCI). |
| DOI: |
10.1109/csci.2017.282 |
| Popis: |
Dividing similar objects into a smaller number of clusters is of importance in many applications. These include search engines, monitoring of academic performance, biology and wireless networks. We first discuss a number of clustering methods. We present a parallel algorithm for the efficient clustering of proteins into groups. The input consists of an n by n distance matrix. This matrix would be built differently for different applications. A two simple points in space can have the Euclidean distance in the matrix. As another example, the Root-Mean-Square-Deviations (RMSD) values can be computed for any two 3-D structures and used and the distance between them. The second step is to utilize parallel processors to calculate a hierarchal cluster of these n items based on this matrix. We have implemented our algorithm and have found it to be scalable. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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