Distributed Formal Concept Analysis Algorithms Based on an Iterative MapReduce Framework

While many existing formal concept analysis algorithms are efficient, they are typically unsuitable for distributed implementation. Taking the MapReduce (MR) framework as the authors' inspiration they introduce a distributed approach for performing formal concept mining. Their method has its novelty in that they use a light-weight MapReduce runtime called twister which is better suited to iterative algorithms than recent distributed approaches. First, they describe the theoretical foundations underpinning their distributed formal concept analysis approach. Second, they provide a representative exemplar of how a classic centralized algorithm can be implemented in a distributed fashion using their methodology: they modify Ganter's classic algorithm by introducing a family of MR algorithms, namely MRGanter and MRGanter+ where the prefix denotes the algorithm's lineage.
Provided by: Waterford Institute Topic: Big Data Date Added: Oct 2012 Format: PDF

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