Generic software for benchmarking formal concept analysis: Orange3 integration
- Autores
- Leutwyler, Nicolas; Lezoche, Mario; Panetto, Hervé; Torres, Diego
- Año de publicación
- 2022
- Idioma
- inglés
- Tipo de recurso
- documento de conferencia
- Estado
- versión publicada
- Descripción
- Thanks to the internet of things (IoT) and cyber physical systems (CPS), we face an incremental growth of the available data, either on the internet or in private databases. This resulted in data mining techniques becoming an essential piece in the information retrieval process. Moreover, trends like the industry 4.0 encourages its usage to support data driven decisions, for instance. Formal Concept Analysis (FCA) is one of the most used techniques in the unsupervised data mining field due to its inherent ability to find patterns between concepts. As a consequence, many applications need the use of fast algorithms to perform the calculations to retrieve either the lattice or the association rules related with the data at their disposal. Due to this, scientists often rely on manually crafted benchmarks to compare how certain algorithms perform under different circumstances. In this work, we propose the architecture of a software to generalize these benchmarks independently of the algorithms, to be integrated in the open source data analysis software Orange3.
This paper is partially supported by funding provided by the STIC AmSud program, Project 22STIC-01.
Facultad de Informática - Materia
-
Ciencias Informáticas
Formal Concept Analysis
benchmarking
metaprogramming
open source - Nivel de accesibilidad
- acceso abierto
- Condiciones de uso
- http://creativecommons.org/licenses/by-nc-sa/4.0/
- Repositorio
.jpg)
- Institución
- Universidad Nacional de La Plata
- OAI Identificador
- oai:sedici.unlp.edu.ar:10915/158887
Ver los metadatos del registro completo
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Generic software for benchmarking formal concept analysis: Orange3 integrationLeutwyler, NicolasLezoche, MarioPanetto, HervéTorres, DiegoCiencias InformáticasFormal Concept Analysisbenchmarkingmetaprogrammingopen sourceThanks to the internet of things (IoT) and cyber physical systems (CPS), we face an incremental growth of the available data, either on the internet or in private databases. This resulted in data mining techniques becoming an essential piece in the information retrieval process. Moreover, trends like the industry 4.0 encourages its usage to support data driven decisions, for instance. Formal Concept Analysis (FCA) is one of the most used techniques in the unsupervised data mining field due to its inherent ability to find patterns between concepts. As a consequence, many applications need the use of fast algorithms to perform the calculations to retrieve either the lattice or the association rules related with the data at their disposal. Due to this, scientists often rely on manually crafted benchmarks to compare how certain algorithms perform under different circumstances. In this work, we propose the architecture of a software to generalize these benchmarks independently of the algorithms, to be integrated in the open source data analysis software Orange3.This paper is partially supported by funding provided by the STIC AmSud program, Project 22STIC-01.Facultad de Informática2022info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdf34-47http://sedici.unlp.edu.ar/handle/10915/158887enginfo:eu-repo/semantics/altIdentifier/isbn/978-950-34-2303-5info:eu-repo/semantics/reference/hdl/10915/158339info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/4.0/Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-10-22T17:22:26Zoai:sedici.unlp.edu.ar:10915/158887Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-10-22 17:22:26.758SEDICI (UNLP) - Universidad Nacional de La Platafalse |
| dc.title.none.fl_str_mv |
Generic software for benchmarking formal concept analysis: Orange3 integration |
| title |
Generic software for benchmarking formal concept analysis: Orange3 integration |
| spellingShingle |
Generic software for benchmarking formal concept analysis: Orange3 integration Leutwyler, Nicolas Ciencias Informáticas Formal Concept Analysis benchmarking metaprogramming open source |
| title_short |
Generic software for benchmarking formal concept analysis: Orange3 integration |
| title_full |
Generic software for benchmarking formal concept analysis: Orange3 integration |
| title_fullStr |
Generic software for benchmarking formal concept analysis: Orange3 integration |
| title_full_unstemmed |
Generic software for benchmarking formal concept analysis: Orange3 integration |
| title_sort |
Generic software for benchmarking formal concept analysis: Orange3 integration |
| dc.creator.none.fl_str_mv |
Leutwyler, Nicolas Lezoche, Mario Panetto, Hervé Torres, Diego |
| author |
Leutwyler, Nicolas |
| author_facet |
Leutwyler, Nicolas Lezoche, Mario Panetto, Hervé Torres, Diego |
| author_role |
author |
| author2 |
Lezoche, Mario Panetto, Hervé Torres, Diego |
| author2_role |
author author author |
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Ciencias Informáticas Formal Concept Analysis benchmarking metaprogramming open source |
| topic |
Ciencias Informáticas Formal Concept Analysis benchmarking metaprogramming open source |
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Thanks to the internet of things (IoT) and cyber physical systems (CPS), we face an incremental growth of the available data, either on the internet or in private databases. This resulted in data mining techniques becoming an essential piece in the information retrieval process. Moreover, trends like the industry 4.0 encourages its usage to support data driven decisions, for instance. Formal Concept Analysis (FCA) is one of the most used techniques in the unsupervised data mining field due to its inherent ability to find patterns between concepts. As a consequence, many applications need the use of fast algorithms to perform the calculations to retrieve either the lattice or the association rules related with the data at their disposal. Due to this, scientists often rely on manually crafted benchmarks to compare how certain algorithms perform under different circumstances. In this work, we propose the architecture of a software to generalize these benchmarks independently of the algorithms, to be integrated in the open source data analysis software Orange3. This paper is partially supported by funding provided by the STIC AmSud program, Project 22STIC-01. Facultad de Informática |
| description |
Thanks to the internet of things (IoT) and cyber physical systems (CPS), we face an incremental growth of the available data, either on the internet or in private databases. This resulted in data mining techniques becoming an essential piece in the information retrieval process. Moreover, trends like the industry 4.0 encourages its usage to support data driven decisions, for instance. Formal Concept Analysis (FCA) is one of the most used techniques in the unsupervised data mining field due to its inherent ability to find patterns between concepts. As a consequence, many applications need the use of fast algorithms to perform the calculations to retrieve either the lattice or the association rules related with the data at their disposal. Due to this, scientists often rely on manually crafted benchmarks to compare how certain algorithms perform under different circumstances. In this work, we propose the architecture of a software to generalize these benchmarks independently of the algorithms, to be integrated in the open source data analysis software Orange3. |
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2022 |
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2022 |
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