Human-supervised clustering of multidimensional data using crowdsourcing.

ABSTRACT: Clustering is a central task in many data analysis applications. However, there is no universally accepted metric to decide the occurrence of clusters. Ultimately, we have to resort to a consensus between experts. The problem is amplified with high-dimensional datasets where classical distances become uninformative and the ability of humans to fully apprehend the distribution of the data is challenged. In this paper, we design a mobile human-computing game as a tool to query human perception for the multidimensional data clustering problem. We propose two clustering algorithms that partially or entirely rely on aggregated human answers and report the results of two experiments conducted on synthetic and real-world datasets. We show that our methods perform on par or better than the most po

SUBMITTER: Butyaev A 

PROVIDER: S-EPMC9128850 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature