Narrow Results

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.

We have developed a generic framework for combining introns from genomicly aligned expressed–sequence–tag clusters with a set of exon predictions to produce alternative transcript predictions. Our current implementation uses ASPIC to generate alternative transcripts from EST mappings. Introns from ASPIC and a set of gene predictions from many diverse gene prediction programs are given to the gene prediction combiner GenePC which then generates alternative consensus splice forms. We evaluated our method on the ENCODE regions of the human genome. In general we see a marked improvement in transcript-level sensitivity due to the fact that more than one transcript per gene may now be predicted. GenePC, which alone is highly specific at the transcript level, balances the lower specificity of ASPIC.