Distributed Formal Concept Analysis Algorithms Based on an Iterative MapReduce Framework
2012 (English)In: Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349, Vol. 7278, 292-308 p.Article in journal (Refereed) Published
While many existing formal concept analysis algorithms are efficient, they are typically unsuitable for distributed implementation. Taking the MapReduce (MR) framework as our inspiration we introduce a distributed approach for performing formal concept mining. Our method has its novelty in that we use a light-weight MapReduce runtime called Twister which is better suited to iterative algorithms than recent distributed approaches. First, we describe the theoretical foundations underpinning our distributed formal concept analysis approach. Second, we provide a representative exemplar of how a classic centralized algorithm can be implemented in a distributed fashion using our methodology: we modify Ganter’s classic algorithm by introducing a family of MR⋆ algorithms, namely MRGanter and MRGanter+ where the prefix denotes the algorithm’s lineage. To evaluate the factors that impact distributed algorithm performance, we compare our MR∗ algorithms with the state-of-the-art. Experiments conducted on real datasets demonstrate that MRGanter+ is efficient, scalable and an appealing algorithm for distributed problems.
Place, publisher, year, edition, pages
Leuven, Belgium: Springer Berlin/Heidelberg, 2012. Vol. 7278, 292-308 p.
Formal Concept Analysis; Distributed Mining; MapReduce
Research subject Applied and Computational Mathematics
IdentifiersURN: urn:nbn:se:kth:diva-174033DOI: 10.1007/978-3-642-29892-9_26ScopusID: 2-s2.0-84864057128ISBN: 978-3-642-29892-9OAI: oai:DiVA.org:kth-174033DiVA: diva2:856942
QC 201509282015-09-262015-09-262015-09-28Bibliographically approved