Astrid Sierens, Isel Grau, Luis Daniel Hernandez, Simeon Michel, Vicky Froyen, Catherine Middag, Ann Nowé
In this master thesis, a new interactive subgroup discovery algorithm is proposed. This method has two main contributions. First, the algorithm allows the expert to intervene during the search process by assessing each subgroup with a degree of appreciation which influences the search process. The second contribution is a diversity parameter that allows the user to avoid that the new subgroups share more than a chosen percentage of instances with already found subgroups. Experiments show that when diversity control is performed, the resulting subgroups have less overlap than the baseline version of the algorithm. Additionally, when using the proposed interactive version of the algorithm, a higher user appreciation of the subgroups is observed. This interactive subgroup discovery algorithm was implemented in the backend of a conversational agent for supporting business analysts in data mining tasks.
Sierens, A, Grau, I, Hernandez, LD, Michel, S, Froyen, V, Middag, C & Nowé, A 2021, 'Thesis Abstract: Interactive Subgroup Discovery for the Conversational Data Governance Platform "Talking to your Data"', Paper presented at 33rd Benelux Conference on Artificial Intelligence and 30th Belgian-Dutch Conference on Machine Learning, Luxembourg, 10/11/21 - 12/11/21 pp. 769-771. <https://luis.leiva.name/tmp/bnaic2021_preproceedings.pdf>
Sierens, A., Grau, I., Hernandez, L. D., Michel, S., Froyen, V., Middag, C., & Nowé, A. (2021). Thesis Abstract: Interactive Subgroup Discovery for the Conversational Data Governance Platform "Talking to your Data". 769-771. Paper presented at 33rd Benelux Conference on Artificial Intelligence and 30th Belgian-Dutch Conference on Machine Learning, Luxembourg. https://luis.leiva.name/tmp/bnaic2021_preproceedings.pdf
@conference{278ecbf91d654580b804b29bc8a13c00,
title = "Thesis Abstract: Interactive Subgroup Discovery for the Conversational Data Governance Platform {"}Talking to your Data{"}",
abstract = "In this master thesis, a new interactive subgroup discovery algorithm is proposed. This method has two main contributions. First, the algorithm allows the expert to intervene during the search process by assessing each subgroup with a degree of appreciation which influences the search process. The second contribution is a diversity parameter that allows the user to avoid that the new subgroups share more than a chosen percentage of instances with already found subgroups. Experiments show that when diversity control is performed, the resulting subgroups have less overlap than the baseline version of the algorithm. Additionally, when using the proposed interactive version of the algorithm, a higher user appreciation of the subgroups is observed. This interactive subgroup discovery algorithm was implemented in the backend of a conversational agent for supporting business analysts in data mining tasks.",
author = "Astrid Sierens and Isel Grau and Hernandez, {Luis Daniel} and Simeon Michel and Vicky Froyen and Catherine Middag and Ann Now{\'e}",
year = "2021",
language = "English",
pages = "769--771",
note = "33rd Benelux Conference on Artificial Intelligence and 30th Belgian-Dutch Conference on Machine Learning : 33rd Benelux Conference on Artificial Intelligence and 30th Belgian-Dutch Conference on Machine Learning, BNAIC/BeneLearn 2021 ; Conference date: 10-11-2021 Through 12-11-2021",
url = "https://bnaic2021.uni.lu/",
}