HaLoop Approach for Concept Generation in Formal Concept Analysis

Abstract

This paper describes an efficient algorithm for formal concepts generation in large formal contexts. While many algorithms exist for concept generation, they are not suitable for generating concepts efficiently on larger contexts. We propose an algorithm named as HaLoopUNCG algorithm based on MapReduce framework that uses a lightweight runtime environment called HaLoop. HaLoop, a modified version of Hadoop MapReduce, suits better for iterative algorithms over large datasets. Our approach uses the features of HaLoop efficiently to generate concepts in an iterative manner. First, we describe the theoretical concepts of formal concept analysis and HaLoop. Second, we provide a detailed representation of our work based on Lindig’s fast concept analysis algorithm using HaLoop and MapReduce framework. The experimental evaluations demonstrate that HaLoopUNCG algorithm is performing better than Hadoop version of upper neighbour concept generation (MRUNCG) algorithm, MapReduce implementation of Ganter’s next closure algorithm and other distributed implementations of concept generation algorithms.