Hierarchical clustering is an important technique to organize big data for exploratory analysis. However, existing one-size-fits-all hierarchical methods often fail meet the diverse needs of different users. To address this challenge, we present interactive steering method visually supervise constrained by utilizing both public knowledge (e.g., Wikipedia) and private from The novelty our approa...

Classical tests for a difference in means control the Type I error rate when groups are defined priori. However, instead via clustering, then applying classical test yields an extremely inflated rate. Notably, this problem persists even if two separate and independent datasets used to define their means. To address problem, article, we propose selective inference approach between clusters. Our ...

Partial orders and directed acyclic graphs are commonly recurring data structures that arise naturally in numerous domains applications used to represent ordered relations between entities the domains. Examples task dependencies a project plan, transaction order distributed ledgers execution sequences of tasks computer programs, just mention few. We study problem preserving hierarchical cluster...

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We develop an algorithm that is fast and scalable in the detection of a nested partition extracted from dendrogram obtained hierarchical clustering multivariate series. Our provides p-value for each clade observed tree. The by computing many bootstrap replicas dissimilarity matrix performing statistical test on difference between associated with given its parent node. prove efficacy our set ben...