Abstract
Clustering of data has numerous applications and has been studied extensively. It is very important in Bioinformatics and data mining. Though many parallel algorithms have been designed, most of algorithms use the CRCW-PRAM or CREW-PRAM models of computing. This paper proposed a parallel EREW deterministic algorithm for hierarchical clustering. Based on algorithms of complete graph and Euclidean minimum spanning tree, the proposed algorithms can cluster n objects with O(p) processors in O(n 2/p) time where 1≤ p ≤ \(\frac{n}{log n}\). Performance comparisons show that our algorithm is the first algorithm that is both without memory conflicts and adaptive.
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Li, Z., Li, K., Xiao, D., Yang, L. (2007). An Adaptive Parallel Hierarchical Clustering Algorithm. In: Perrott, R., Chapman, B.M., Subhlok, J., de Mello, R.F., Yang, L.T. (eds) High Performance Computing and Communications. HPCC 2007. Lecture Notes in Computer Science, vol 4782. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-75444-2_15
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DOI: https://doi.org/10.1007/978-3-540-75444-2_15
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