Distributed Formal Concept Analysis Algorithms Based on an Iterative MapReduce Framework

TL;DR

This paper introduces a novel distributed formal concept analysis method using the Twister MapReduce framework, adapting classic algorithms for better scalability and efficiency in distributed environments.

Contribution

It presents a new distributed approach for formal concept analysis based on an iterative MapReduce framework and modifies Ganter's algorithm for distributed execution.

Findings

MRGanter+ is efficient and scalable for distributed datasets.

The approach outperforms existing distributed algorithms in experiments.

The method effectively adapts classic algorithms for distributed formal concept analysis.

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 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.