Turning CARTwheels: An Alternating Algorithm for Mining Redescriptions

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dc.creatorKumar, Deept
dc.creatorRamakrishnan, Naren
dc.creatorPotts, Malcolm
dc.creatorHelm, Richard F.
dc.date2003-11-27
dc.date.accessioned2026-07-07T03:20:39Z
dc.date.available2026-07-07T03:20:39Z
dc.descriptionWe present an unusual algorithm involving classification trees where two trees are grown in opposite directions so that they are matched at their leaves. This approach finds application in a new data mining task we formulate, called "redescription mining". A redescription is a shift-of-vocabulary, or a different way of communicating information about a given subset of data; the goal of redescription mining is to find subsets of data that afford multiple descriptions. We highlight the importance of this problem in domains such as bioinformatics, which exhibit an underlying richness and diversity of data descriptors (e.g., genes can be studied in a variety of ways). Our approach helps integrate multiple forms of characterizing datasets, situates the knowledge gained from one dataset in the context of others, and harnesses high-level abstractions for uncovering cryptic and subtle features of data. Algorithm design decisions, implementation details, and experimental results are presented.
dc.identifierhttps://arxiv.org/abs/cs/0311048
dc.identifierhttp://arxiv.org/abs/cs/0311048
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/31900
dc.subjectComputational Engineering, Finance, and Science
dc.subjectArtificial Intelligence
dc.subjectH.2.8
dc.titleTurning CARTwheels: An Alternating Algorithm for Mining Redescriptions
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