Clustering with Fast, Automated and Reproducible assessment applied to longitudinal neural tracking

19 Mar 2020 Hanlin Zhu Xue Li Liuyang Sun Fei He Zhengtuo Zhao Lan Luan Ngoc Mai Tran Chong Xie

Across many areas, from neural tracking to database entity resolution, manual assessment of clusters by human experts presents a bottleneck in rapid development of scalable and specialized clustering methods. To solve this problem we develop C-FAR, a novel method for Fast, Automated and Reproducible assessment of multiple hierarchical clustering algorithms simultaneously... (read more)

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