TY - GEN
T1 - q-Divergence Regularization of Bezdek-Type Fuzzy Clustering for Categorical Multivariate Data
AU - Kanzawa, Yuchi
N1 - Publisher Copyright:
© 2021, Springer Nature Switzerland AG.
PY - 2021
Y1 - 2021
N2 - In this paper, the q-divergence-regularized Bezdek-type fuzzy clustering approach is proposed for categorical multivariate data. Because the approach proposed here reduces to the conventional methods via appropriate control of the fuzzification parameters, it is considered as a generalization. Further, numerical experiments were conducted to show that the proposed method outperformed the conventional method in terms of clustering accuracy.
AB - In this paper, the q-divergence-regularized Bezdek-type fuzzy clustering approach is proposed for categorical multivariate data. Because the approach proposed here reduces to the conventional methods via appropriate control of the fuzzification parameters, it is considered as a generalization. Further, numerical experiments were conducted to show that the proposed method outperformed the conventional method in terms of clustering accuracy.
UR - http://www.scopus.com/inward/record.url?scp=85115827746&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85115827746&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-85529-1_18
DO - 10.1007/978-3-030-85529-1_18
M3 - Conference contribution
AN - SCOPUS:85115827746
SN - 9783030855284
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 218
EP - 230
BT - Modeling Decisions for Artificial Intelligence - 18th International Conference, MDAI 2021, Proceedings
A2 - Torra, Vicenç
A2 - Narukawa, Yasuo
PB - Springer Science and Business Media Deutschland GmbH
T2 - 18th International Conference on Modeling Decisions for Artificial Intelligence, MDAI 2021
Y2 - 27 September 2021 through 30 September 2021
ER -