Formal Concept Analysis Approximations via Atomic Priming

Ruairí de Fréin

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    Abstract

    Formal Concept Analysis (FCA) looks to decompose a matrix of objects-attributes into a set of sparse matrices capturing the underlying structure of a formal context. We propose a Rank Reduction (RR) method to prime approximate FCAs, namely RRFCA. While many existing FCA algorithms are complete, lectic ordering of the lattice may not minimize search/decomposition time. Initially, RRFCA decompositions are not unique or complete; however, a set of good closures with high support is learned quickly, and then, made complete. RRFCA has its novelty in that we propose a new multiplicative two-stage method. First, we describe the theoretical foundations underpinning our RR approach. Second, we provide a representative exemplar, showing how RRFCA can be implemented. Further experiments demonstrate that RRFCA methods are efficient, scalable and yield time-savings. We demonstrate the resulting methods lend themselves to parallelization.
    Original languageEnglish
    Title of host publicationFormal Concept Analysis
    PublisherSpringer
    Pages92-108
    Volume7880
    ISBN (Print)978-3-642-38316-8
    Publication statusPublished - 2013

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