Extracting concepts from triadic contexts using Binary Decision Diagram
Due to the high complexity of real problems, a considerable amount of research that deals with high volumes of information has emerged. The literature has considered new applications of data analysis for high dimensional environments in order to manage the difficulty in extracting knowledge from a d...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Graz University of Technology
2022-06-01
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| Series: | Journal of Universal Computer Science |
| Subjects: | |
| Online Access: | https://lib.jucs.org/article/67953/download/pdf/ |
| _version_ | 1818242450492227584 |
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| author | Julio Cesar Vale Neves Luiz Enrique Zarate Mark Alan Junho Song |
| author_facet | Julio Cesar Vale Neves Luiz Enrique Zarate Mark Alan Junho Song |
| author_sort | Julio Cesar Vale Neves |
| collection | DOAJ |
| description | Due to the high complexity of real problems, a considerable amount of research that deals with high volumes of information has emerged. The literature has considered new applications of data analysis for high dimensional environments in order to manage the difficulty in extracting knowledge from a database, especially with the increase in social and professional networks. Tri- adic Concept Analysis (TCA) is a technique used in the applied mathematical area of data analysis. Its main purpose is to enable knowledge extraction from a context that contains objects, attributes, and conditions in a hierarchical and systematized representation. There are several algorithms that can extract concepts, but they are inefficient when applied to large datasets because the compu- tational costs are exponential. The objective of this paper is to add a new data structure, binary decision diagrams (BDD), in the TRIAS algorithm and retrieve triadic concepts for high dimen- sional contexts. BDD was used to characterize formal contexts, objects, attributes, and conditions. Moreover, to reduce the computational resources needed to manipulate a high-volume of data, the usage of BDD was implemented to simplify and represent data. The results show that this method has a considerably better speedup when compared to the original algorithm. Also, our approach discovered concepts that were previously unachievable when addressing high dimensional contexts. |
| first_indexed | 2024-12-12T13:45:26Z |
| format | Article |
| id | doaj.art-1e5fcc38c4f3470eabfc0ef9c1d1ede5 |
| institution | Directory Open Access Journal |
| issn | 0948-6968 |
| language | English |
| last_indexed | 2024-12-12T13:45:26Z |
| publishDate | 2022-06-01 |
| publisher | Graz University of Technology |
| record_format | Article |
| series | Journal of Universal Computer Science |
| spelling | doaj.art-1e5fcc38c4f3470eabfc0ef9c1d1ede52022-12-22T00:22:42ZengGraz University of TechnologyJournal of Universal Computer Science0948-69682022-06-0128659161910.3897/jucs.6795367953Extracting concepts from triadic contexts using Binary Decision DiagramJulio Cesar Vale Neves0Luiz Enrique Zarate1Mark Alan Junho Song2Pontificia Universidade Catolica de Minas Gerais (PUC Minas)Pontificia Universidade Catolica de Minas Gerais (PUC Minas)Pontificia Universidade Catolica de Minas Gerais (PUC Minas)Due to the high complexity of real problems, a considerable amount of research that deals with high volumes of information has emerged. The literature has considered new applications of data analysis for high dimensional environments in order to manage the difficulty in extracting knowledge from a database, especially with the increase in social and professional networks. Tri- adic Concept Analysis (TCA) is a technique used in the applied mathematical area of data analysis. Its main purpose is to enable knowledge extraction from a context that contains objects, attributes, and conditions in a hierarchical and systematized representation. There are several algorithms that can extract concepts, but they are inefficient when applied to large datasets because the compu- tational costs are exponential. The objective of this paper is to add a new data structure, binary decision diagrams (BDD), in the TRIAS algorithm and retrieve triadic concepts for high dimen- sional contexts. BDD was used to characterize formal contexts, objects, attributes, and conditions. Moreover, to reduce the computational resources needed to manipulate a high-volume of data, the usage of BDD was implemented to simplify and represent data. The results show that this method has a considerably better speedup when compared to the original algorithm. Also, our approach discovered concepts that were previously unachievable when addressing high dimensional contexts.https://lib.jucs.org/article/67953/download/pdf/Formal Concept AnalysisBinary Decision Diagram |
| spellingShingle | Julio Cesar Vale Neves Luiz Enrique Zarate Mark Alan Junho Song Extracting concepts from triadic contexts using Binary Decision Diagram Journal of Universal Computer Science Formal Concept Analysis Binary Decision Diagram |
| title | Extracting concepts from triadic contexts using Binary Decision Diagram |
| title_full | Extracting concepts from triadic contexts using Binary Decision Diagram |
| title_fullStr | Extracting concepts from triadic contexts using Binary Decision Diagram |
| title_full_unstemmed | Extracting concepts from triadic contexts using Binary Decision Diagram |
| title_short | Extracting concepts from triadic contexts using Binary Decision Diagram |
| title_sort | extracting concepts from triadic contexts using binary decision diagram |
| topic | Formal Concept Analysis Binary Decision Diagram |
| url | https://lib.jucs.org/article/67953/download/pdf/ |
| work_keys_str_mv | AT juliocesarvaleneves extractingconceptsfromtriadiccontextsusingbinarydecisiondiagram AT luizenriquezarate extractingconceptsfromtriadiccontextsusingbinarydecisiondiagram AT markalanjunhosong extractingconceptsfromtriadiccontextsusingbinarydecisiondiagram |