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An article from the Telecommunications Software & Systems Group, Waterford Institute of Technology, Ireland describing 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. Accepted for publication at the International Conference for Formal Concept Analysis 2012. Project participants: Biao Xu, Ruairí de Fréin, Eric Robson, Mícheál Ó Foghlú Ruairí de Fréin: rdefrein (at) gmail (dot) com bibtex: @incollection{ year={2012}, isbn={978-3-642-29891-2}, booktitle={Formal Concept Analysis}, volume={7278}, series={Lecture Notes in Computer Science}, editor={Domenach, Florent and Ignatov, DmitryI. and Poelmans, Jonas}, doi={10.1007/978-3-642-29892-9_26}, title={Distributed Formal Concept Analysis Algorithms Based on an Iterative MapReduce Framework}, url={http://dx.doi.org/10.1007/978-3-642-29892-9_26}, publisher={Springer Berlin Heidelberg}, keywords={Formal Concept Analysis; Distributed Mining; MapReduce}, author={Xu, Biao and Fréin, Ruairí and Robson, Eric and Ó Foghlú, Mícheál}, pages={292-308} } DOWNLOAD The article Arxiv: http://arxiv.org/abs/1210.2401]]>
An article from the Telecommunications Software & Systems Group, Waterford Institute of Technology, Ireland describing 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. Accepted for publication at the International Conference for Formal Concept Analysis 2012. Project participants: Biao Xu, Ruairí de Fréin, Eric Robson, Mícheál Ó Foghlú Ruairí de Fréin: rdefrein (at) gmail (dot) com bibtex: @incollection{ year={2012}, isbn={978-3-642-29891-2}, booktitle={Formal Concept Analysis}, volume={7278}, series={Lecture Notes in Computer Science}, editor={Domenach, Florent and Ignatov, DmitryI. and Poelmans, Jonas}, doi={10.1007/978-3-642-29892-9_26}, title={Distributed Formal Concept Analysis Algorithms Based on an Iterative MapReduce Framework}, url={http://dx.doi.org/10.1007/978-3-642-29892-9_26}, publisher={Springer Berlin Heidelberg}, keywords={Formal Concept Analysis; Distributed Mining; MapReduce}, author={Xu, Biao and Fréin, Ruairí and Robson, Eric and Ó Foghlú, Mícheál}, pages={292-308} } DOWNLOAD The article Arxiv: http://arxiv.org/abs/1210.2401]]> Wed, 29 May 2013 05:27:23 GMT /slideshow/icfca12-bx-rdf/22123338 ruairidefrein5@slideshare.net(ruairidefrein5) Distributed Formal Concept Analysis Algorithms Based on an Iterative MapReduce Framework ruairidefrein5 An article from the Telecommunications Software & Systems Group, Waterford Institute of Technology, Ireland describing 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. Accepted for publication at the International Conference for Formal Concept Analysis 2012. Project participants: Biao Xu, Ruairí de Fréin, Eric Robson, Mícheál Ó Foghlú Ruairí de Fréin: rdefrein (at) gmail (dot) com bibtex: @incollection{ year={2012}, isbn={978-3-642-29891-2}, booktitle={Formal Concept Analysis}, volume={7278}, series={Lecture Notes in Computer Science}, editor={Domenach, Florent and Ignatov, DmitryI. and Poelmans, Jonas}, doi={10.1007/978-3-642-29892-9_26}, title={Distributed Formal Concept Analysis Algorithms Based on an Iterative MapReduce Framework}, url={http://dx.doi.org/10.1007/978-3-642-29892-9_26}, publisher={Springer Berlin Heidelberg}, keywords={Formal Concept Analysis; Distributed Mining; MapReduce}, author={Xu, Biao and Fréin, Ruairí and Robson, Eric and Ó Foghlú, Mícheál}, pages={292-308} } DOWNLOAD The article Arxiv: http://arxiv.org/abs/1210.2401 <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/icfca12bxrdf-130529052723-phpapp01-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> An article from the Telecommunications Software &amp; Systems Group, Waterford Institute of Technology, Ireland describing 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&#39;s classic algorithm by introducing a family of MR* algorithms, namely MRGanter and MRGanter+ where the prefix denotes the algorithm&#39;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. Accepted for publication at the International Conference for Formal Concept Analysis 2012. Project participants: Biao Xu, Ruairí de Fréin, Eric Robson, Mícheál Ó Foghlú Ruairí de Fréin: rdefrein (at) gmail (dot) com bibtex: @incollection{ year={2012}, isbn={978-3-642-29891-2}, booktitle={Formal Concept Analysis}, volume={7278}, series={Lecture Notes in Computer Science}, editor={Domenach, Florent and Ignatov, DmitryI. and Poelmans, Jonas}, doi={10.1007/978-3-642-29892-9_26}, title={Distributed Formal Concept Analysis Algorithms Based on an Iterative MapReduce Framework}, url={http://dx.doi.org/10.1007/978-3-642-29892-9_26}, publisher={Springer Berlin Heidelberg}, keywords={Formal Concept Analysis; Distributed Mining; MapReduce}, author={Xu, Biao and Fréin, Ruairí and Robson, Eric and Ó Foghlú, Mícheál}, pages={292-308} } DOWNLOAD The article Arxiv: http://arxiv.org/abs/1210.2401
Distributed Formal Concept Analysis Algorithms Based on an Iterative MapReduce Framework from Ruairi de Frein
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