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. In both cases, the results were positive.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Yager, R.R.: A new approach to the summarization of data. Inf. Sci. 28, 69–86 (1982)
Fontenla-Seco, Y., Bugarin, A., Lama, M.: Fuzzy temporal protoforms for the quantitative description of processes in natural language. In: IEEE International Conference on Fuzzy Systems, pp. 1–6 (2021). http://doi.org/10.1109/FUZZ45933.2021.9494444
Cornejo, M.E., Medina, J., Rubio-Manzano, C.: Linguistic descriptions of data via fuzzy formal concept analysis. In: Computational Intelligence and Mathematics for Tackling Complex Problems, pp. 119–125. Springer, Berlin (2022). http://doi.org/10.1007/978-3-030-74970-5_14
Duraj, A., Szczepaniak, P.S.: Linguistic summaries using interval-valued fuzzy representation of imprecise information—an innovative tool for detecting outliers. In: International Conference on Computational Science (ICCS 2021), pp. 500–513. Springer, Berlin (2021). http://doi.org/10.1007/978-3-030-77980-1_38
Nguyen, C.H., Pham, T.L., Nguyen, T.N., Ho, C.H., Nguyen, T.A.: The linguistic summarization and the interpretability, scalability of fuzzy representations of multilevel semantic structures of word-domains. Microprocess. Microsyst. (2021). https://doi.org/10.1016/J.MICPRO.2020.103641
To, N.D., Reformat, M.Z., Yager, R.R.: Question-answering system with linguistic summarization. In: IEEE International Conference on Fuzzy Systems, pp. 1–8 (2021). http://doi.org/10.1109/FUZZ45933.2021.9494389
Pérez, I., Piñero, P.Y., Bello, R., Acuña, L.A., García, R.: Linguistic summaries generation with hybridization method based on rough and fuzzy sets. In: International Joint Conference on Rough Sets, pp. 385–397. Springer, Berlin (2020). http://doi.org/10.1007/978-3-030-52705-1_29
Kacprzyk, J., Yager, R., Merigo, J.M.: Towards human-centric aggregation via ordered weighted aggregation operators and linguistic data summaries: a new perspective on Zadeh’s inspirations. IEEE Comput. Intell. Mag. 14, 16–30 (2019). https://doi.org/10.1109/MCI.2018.2881641
Rodríguez, C.R., Peña, M., Zuev, D.S.: Extracting composite summaries from qualitative data. In: VII International Workshop on Artificial Intelligence and Pattern Recognition. Springer, Berlin (2021). http://doi.org/10.1007/978-3-030-89691-1_26
Yager, R.R., Reformat, M.Z., To, N.D.: Drawing on the iPad to input fuzzy sets with an application to linguistic data science. Inf. Sci. 479, 277–291 (2019). https://doi.org/10.1016/J.INS.2018.11.048
Ramos-Soto, A., Martin-Rodilla, P.: Enriching linguistic descriptions of data: a framework for composite protoforms. In: Fuzzy Sets and Systems, vol. 407, pp. 1–26 (2021). http://doi.org/10.1016/j.fss.2019.11.013
Heble-Lahera, C., Cascallar-Fuentes, A., Ramos-Soto, A., Diz, A.B.: Empirical study of fuzzy quantification models for linguistic descriptions of meteorological data. In: IEEE International Conference on Fuzzy Systems (2020). http://doi.org/10.1109/FUZZ48607.2020.9177716
Peláez-Aguilera, M.D., Espinilla, M., Fernández Olmo, M.R., Medina, J.: Fuzzy linguistic protoforms to summarize heart rate streams of patients with ischemic heart disease. Complexity (2019). http://doi.org/10.1155/2019/2694126
Genç, S., Akay, D., Boran, F.E., Yager, R.R.: Linguistic summarization of fuzzy social and economic networks: an application on the international trade network. Soft. Comput. 24, 1511–1527 (2019). https://doi.org/10.1007/S00500-019-03982-9
Wilbik, A., Gilsing, R., Turetken, O., Ozkan, B., Grefen, P.: Intentional linguistic summaries for collaborative business model radars. In: 2020 IEEE International Conference on Fuzzy Systems, pp. 1–7 (2020). http://doi.org/10.1109/FUZZ48607.2020.9177587
Moreno-Garcia, J., Jimenez-Linares, L., Rodriguez-Benitez, L.: Automatic generation of linguistic descriptions of electricity consumption in the buildings of a large institution. In: Computational Intelligence and Mathematics for Tackling Complex Problems, vol. 3, pp. 101–109. Springer, Berlin (2022). http://doi.org/10.1007/978-3-030-74970-5_12
Moreno-Garcia, J., Jimenez-Linares, L., Liu, J., Rodriguez-Benitez, L.: Generation of linguistic descriptions for daily noise pollution in urban areas. In: IEEE International Conference on Fuzzy Systems, pp. 1–6 (2021). http://doi.org/10.1109/FUZZ45933.2021.9494388
Pérez, I., Piñero, P.Y., García, R., Bello, R., Acuña, L.A.: Discovering fails in software projects planning based on linguistic summaries. In: International Joint Conference on Rough Sets, pp. 365–375. Springer, Berlin (2020). http://doi.org/10.1007/978-3-030-52705-1_27
Zadeh, L.A.: A prototype-centered approach to adding deduction capability to search engines—the concept of protoform. In: Annual Conference of the North American Fuzzy Information Processing Society—NAFIPS (2002). http://doi.org/10.1109/NAFIPS.2002.1018115
Kacprzyk, J., Zadrozny, S.: Linguistic database summaries and their protoforms: towards natural language based knowledge discovery tools. In: Information Sciences, vol. 173 (2005). http://doi.org/10.1016/j.ins.2005.03.002
Trivino, G., Sugeno, M.: Towards linguistic descriptions of phenomena. Int. J. Approximate Reasoning 54, 22–34 (2013). https://doi.org/10.1016/j.ijar.2012.07.004
Smits, G., Nerzic, P., Pivert, O., Lesot, M.J.: Efficient generation of reliable estimated linguistic summaries. In: IEEE International Conference on Fuzzy Systems (2018). http://doi.org/10.1109/FUZZ-IEEE.2018.8491604
Mann, W.C., Thompson, S.A.: Rhetorical structure theory: toward a functional theory of text organization. Text 8, 243–281 (1988)
Hou, S., Zhang, S., Fei, C.: Rhetorical structure theory: a comprehensive review of theory, parsing methods and applications. In: Expert Systems with Applications, vol. 157 (2020). http://doi.org/10.1016/j.eswa.2020.113421
Agrawal, R., Srikant, R.: A fast algorithm for mining association rules. In: Proceedings of the 20th International Conference on Very Large Databases, VLDB, pp. 487–499 (1994)
Savasere, A., Omiecinski, E., Navathe, S.: An efficient algorithm for mining association rules in large databases. In: Proceedings of the 21st International Conference on Very Large Data Bases, pp. 432–443, Zurich (1995)
Zaki, M.J.: Scalable algorithms for association mining. IEEE Trans. Knowl. Data Eng. 12, 372–390 (2000). https://doi.org/10.1109/69.846291
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this chapter
Cite this chapter
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
Download citation
DOI: https://doi.org/10.1007/978-3-030-97269-1_5
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-97268-4
Online ISBN: 978-3-030-97269-1
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)