Distributed Formal Concept Analysis Algorithms Based on an Iterative MapReduce Framework

Abstract

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 $\mbox{MR}^\star$ 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 $\mbox{MR}^{*}$ 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.