title: Practical data mining and machine learning for optics applications: introduction to the feature issue.
abstract: Data mining algorithms utilize search techniques to explore hidden patterns and correlations in the data, which otherwise require a tremendous amount of human time to explore. This feature issue explores the use of such techniques to help understand the data, build better simulators, explain outlier behavior, and build better predictive models. We hope that this issue will spur discussions and expose a set of tools that can be useful to the optics community.
authors.affiliation: Lawrence Livermore National Laboratory, Livermore California 94550, USA. abdulla1@llnl.gov
authors.forename: Ghaleb
authors.initials: G
authors.lastname: Abdulla
authors.forename: Abdul
authors.initials: A
authors.lastname: Awwal
authors.forename: Kirk
authors.initials: K
authors.lastname: Borne
authors.forename: Tin Kam
authors.initials: TK
authors.lastname: Ho
authors.forename: W Thomas
authors.initials: WT
authors.lastname: Vestrand