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Author Parida, Laxmi ♦ Ramakrishnan, Naren
Source CiteSeerX
Content type Text
File Format PDF
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Subject Keyword Unifies Consideration ♦ Broad Scope ♦ Redescription Mining ♦ Special Case ♦ Structure Theory ♦ Re-description Mining Solution ♦ Re-description Mining ♦ Strong Possibility Re-sults ♦ Propositional Vocabulary ♦ Constructive Induction ♦ Primary Contribution ♦ Conceptual Clustering ♦ Many Problem ♦ Various Bias ♦ Mining Redescriptions ♦ Machine Learning Community ♦ New Line ♦ Intrinsic Structure ♦ Logical Formula Discovery ♦ Present Several Scenario
Description We introduce a new data mining problem—redescription mining—that unifies considerations of conceptual clustering, constructive induction, and logical formula discovery. Re-description mining begins with a collection of sets, views it as a propositional vocabulary, and identifies clusters of data that can be defined in at least two ways using this vocabulary. The primary contributions of this paper are conceptual and theoretical: (i) we formally study the space of redescriptions underlying a dataset and characterize their intrinsic structure, (ii) we identify impossibility as well as strong possibility re-sults about when mining redescriptions is feasible, (iii) we present several scenarios of how we can custom-build re-description mining solutions for various biases, and (iv) we outline how many problems studied in the larger machine learning community are really special cases of redescription mining. By highlighting its broad scope and relevance, we aim to establish the importance of redescription mining and make the case for a thrust in this new line of research.
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research
Education Level UG and PG ♦ Career/Technical Study
Learning Resource Type Article
Publisher Date 2005-01-01