TY - GEN
T1 - Fuzzy clustering based on α-divergence for spherical data and for categorical multivariate data
AU - Kanzawa, Yuchi
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/11/25
Y1 - 2015/11/25
N2 - This paper presents two clustering algorithms based on α-divergence between memberships and variables that control cluster sizes: one is for spherical data and the other for categorical multivariate data. First, this paper shows that a conventional method for vectorial data can be interpreted as the regularization of another conventional method with α-divergence. Second, with this interpretation, a spherical clustering algorithm based on α-divergence is derived from an optimization problem built by regularizing a conventional method with α-divergence. Third, this paper connects the facts that the α-divergence is a generalization of Kullback-Leibler (KL)-divergence, and that three conventional co-clustering methods are based on KL-divergence. Based on these facts, a co-clustering algorithm based on α-divergence is derived from an optimization problem built by extending the KL-divergence in conventional methods to α-divergence. This paper also demonstrates some numerical examples for the proposed methods.
AB - This paper presents two clustering algorithms based on α-divergence between memberships and variables that control cluster sizes: one is for spherical data and the other for categorical multivariate data. First, this paper shows that a conventional method for vectorial data can be interpreted as the regularization of another conventional method with α-divergence. Second, with this interpretation, a spherical clustering algorithm based on α-divergence is derived from an optimization problem built by regularizing a conventional method with α-divergence. Third, this paper connects the facts that the α-divergence is a generalization of Kullback-Leibler (KL)-divergence, and that three conventional co-clustering methods are based on KL-divergence. Based on these facts, a co-clustering algorithm based on α-divergence is derived from an optimization problem built by extending the KL-divergence in conventional methods to α-divergence. This paper also demonstrates some numerical examples for the proposed methods.
KW - Atmospheric measurements
KW - Clustering algorithms
KW - Clustering methods
KW - Entropy
KW - Machine learning algorithms
KW - Optimization
KW - Particle measurements
UR - http://www.scopus.com/inward/record.url?scp=84975760671&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84975760671&partnerID=8YFLogxK
U2 - 10.1109/FUZZ-IEEE.2015.7337853
DO - 10.1109/FUZZ-IEEE.2015.7337853
M3 - Conference contribution
AN - SCOPUS:84975760671
T3 - IEEE International Conference on Fuzzy Systems
BT - FUZZ-IEEE 2015 - IEEE International Conference on Fuzzy Systems
A2 - Yazici, Adnan
A2 - Pal, Nikhil R.
A2 - Ishibuchi, Hisao
A2 - Tutmez, Bulent
A2 - Lin, Chin-Teng
A2 - Sousa, Joao M. C.
A2 - Kaymak, Uzay
A2 - Martin, Trevor
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2015
Y2 - 2 August 2015 through 5 August 2015
ER -