КФУ. Карточка публикации. Algorithms for Linguistic Description of Categorical Data

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ALGORITHMS FOR LINGUISTIC DES&SHY;CRIPTION OF CATEGORICAL DATA

Форма представления

Статьи в зарубежных журналах и сборниках

Год публикации

2022

Язык

английский

Авторы, сотрудники КФУ

Зуев Денис Сергеевич, автор

Авторы, аспиранты КФУ

Родригес Родригес Карлос Рафаэль , автор

Библиографическое описание на языке оригинала

Rodríguez Rodríguez, C.R., Zuev, D.S., Peña Abreu, M. (2022). Algorithms for Linguistic Description of Categorical Data. In: Piñero Pérez, P.Y., Bello Pérez, R.E., Kacprzyk, J. (eds) Artificial Intelligence in Project Management and Making Decisions. UCIENCIA 2021. Studies in Computational Intelligence, vol 1035. Springer, Cham. https://doi.org/10.1007/978-3-030-97269-1_5

Аннотация

The paper proposes a method that comprises five algorithms for producing composite linguistic summaries from categorical data. The generated composite summaries reflect Evidence, Contrast, or Emphasis relations between at least two constituent summaries. The constituent summaries are instances of the LDS classical protoforms created, in this case, with frequent L1 item sets and association rules obtained from applying an association rule mining algorithm. In order to verify the feasibility of implementing the method, we performed a use case with a dataset of 2128 cases of the Economic Chamber of the Provincial People?s Court of Havana. The results were consistent with expectations, obtaining 18 Evidence relations, 11 Contrast relations, and 16 Emphasis relations. Furthermore, we evaluated the interpretability of the composite summaries obtained in the use case. Specifically, we measured the accuracy of identifying the relation type implicit in the summary and their understandability.

Ключевые слова

Association rules, Linguistic data summarization, Linguistic descriptions of data

Название журнала

Studies in Computational Intelligence

URL

https://link.springer.com/chapter/10.1007/978-3-030-97269-1_5

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https://repository.kpfu.ru/?p_id=266732

Полная запись метаданных

Поле DC	Значение	Язык
dc.contributor.author	Зуев Денис Сергеевич	ru_RU
dc.contributor.author	Родригес Родригес Карлос Рафаэль	ru_RU
dc.date.accessioned	2022-01-01T00:00:00Z	ru_RU
dc.date.available	2022-01-01T00:00:00Z	ru_RU
dc.date.issued	2022	ru_RU
dc.identifier.citation	Rodríguez Rodríguez, C.R., Zuev, D.S., Peña Abreu, M. (2022). Algorithms for Linguistic Description of Categorical Data. In: Piñero Pérez, P.Y., Bello Pérez, R.E., Kacprzyk, J. (eds) Artificial Intelligence in Project Management and Making Decisions. UCIENCIA 2021. Studies in Computational Intelligence, vol 1035. Springer, Cham. https://doi.org/10.1007/978-3-030-97269-1_5	ru_RU
dc.identifier.uri	https://repository.kpfu.ru/?p_id=266732	ru_RU
dc.description.abstract	Studies in Computational Intelligence	ru_RU
dc.description.abstract	The paper proposes a method that comprises five algorithms for producing composite linguistic summaries from categorical data. The generated composite summaries reflect Evidence, Contrast, or Emphasis relations between at least two constituent summaries. The constituent summaries are instances of the LDS classical protoforms created, in this case, with frequent L1 item sets and association rules obtained from applying an association rule mining algorithm. In order to verify the feasibility of implementing the method, we performed a use case with a dataset of 2128 cases of the Economic Chamber of the Provincial People?s Court of Havana. The results were consistent with expectations, obtaining 18 Evidence relations, 11 Contrast relations, and 16 Emphasis relations. Furthermore, we evaluated the interpretability of the composite summaries obtained in the use case. Specifically, we measured the accuracy of identifying the relation type implicit in the summary and their understandability.	ru_RU
dc.language.iso	ru	ru_RU
dc.subject	Association rules	ru_RU
dc.subject	Linguistic data summarization	ru_RU
dc.subject	Linguistic descriptions of data	ru_RU
dc.title	Algorithms for Linguistic Description of Categorical Data	ru_RU
dc.type	Статьи в зарубежных журналах и сборниках	ru_RU