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

Sept. 23, 2022, 1:13 a.m. | Christian A. Scholbeck, Henri Funk, Giuseppe Casalicchio

stat.ML 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

Machine Learning Product Manager (Canada, Remote)

@ FreshBooks | Canada

Data Engineer

@ Amazon.com | Irvine, California, USA

Senior Autonomy Behavior II, Performance Assessment Engineer

@ Cruise LLC | San Francisco, CA

Senior Data Analytics Engineer

@ Intercom | Dublin, Ireland

Data Analyst Intern

@ ADDX | Singapore

Data Science Analyst - Consumer

@ Yelp | London, England, United Kingdom

Senior Data Analyst - Python+Hadoop

@ Capco | India - Bengaluru

DevOps Engineer, Data Team

@ SingleStore | Hyderabad, India

Software Engineer (Machine Learning, AI Platform)

@ Phaidra | Remote

Sr. UI/UX Designer - Artificial Intelligence (ID:1213)

@ Truelogic Software | Remote, anywhere in LATAM

Analytics Engineer

@ carwow | London, England, United Kingdom

HRIS Data Analyst

@ SecurityScorecard | Remote