Parallel Hierarchical Clustering in Linearithmic Time for Large-Scale Sequence Analysis

@article{Mao2015ParallelHC,
  title={Parallel Hierarchical Clustering in Linearithmic Time for Large-Scale Sequence Analysis},
  author={Qi Mao and Wei Zheng and Li Wang and Yunpeng Cai and Volker Mai and Yijun Sun},
  journal={2015 IEEE International Conference on Data Mining},
  year={2015},
  pages={310-319}
}
The rapid development of sequencing technology has led to an explosive accumulation of genomics data. Clustering is often the first step to perform in sequence analysis, and hierarchical clustering is one of the most commonly used approaches for this purpose. However, the standard hierarchical clustering method scales poorly due to its quadratic time and space complexities stemming mainly from the need of computing and storing a pairwise distance matrix. It is thus necessary to minimize the… CONTINUE READING

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