Web: http://arxiv.org/abs/2209.10578

Sept. 23, 2022, 1:11 a.m. | Christian A. Scholbeck, Henri Funk, Giuseppe Casalicchio

cs.LG updates on arXiv.org arxiv.org

A clustering outcome for high-dimensional data is typically interpreted via
post-processing, involving dimension reduction and subsequent visualization.
This destroys the meaning of the data and obfuscates interpretations. We
propose algorithm-agnostic interpretation methods to explain clustering
outcomes in reduced dimensions while preserving the integrity of the data. The
permutation feature importance for clustering represents a general framework
based on shuffling feature values and measuring changes in cluster assignments
through custom score functions. The individual conditional expectation for
clustering indicates observation-wise changes …

algorithm arxiv clustering

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