title: Visual classification: expert knowledge guides machine learning.
abstract: Humans use intuition and experience to classify everything they perceive, but only if the distinguishing patterns are visible. Machine-learning algorithms can learn class information from data sets, but the created classes' meaning isn't always clear. A proposed mixed-initiative approach combines intuitive visualizations with machine learning to tap into the strengths of human and machine classification. The use of visualizations in an expert-guided clustering technique allows the display of complex data sets in a way that allows human input into machine clustering. Test participants successfully employed this technique to classify analytic activities using behavioral observations of a creative-analysis task. The results demonstrate how visualization of the machine-learned classification can help users create more robust and intuitive categories.
authors.forename: J
authors.initials: J
authors.lastname: MacInnes
authors.forename: S
authors.initials: S
authors.lastname: Santosa
authors.forename: W
authors.initials: W
authors.lastname: Wright
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D016000:Cluster Analysis; D006801:Humans; D007091:Image Processing, Computer-Assisted; D020407:Internet; D014584:User-Computer Interface