2024-03-28T13:47:48Z
https://nagoya.repo.nii.ac.jp/oai
oai:nagoya.repo.nii.ac.jp:00018609
2023-01-16T04:07:00Z
320:502:503
A Clustering Method for Geometric Data based on Approximation using Conformal Geometric Algebra
Minh, Tuan Pham
54085
Tachibana, Kanta
54086
Yoshikawa, Tomohiro
54087
Furuhashi, Takeshi
54088
conformal geometric algebra
hyper-sphere
inner product
distance
clustering
Clustering is one of the most useful methods for understanding similarity among data. However, most conventional clustering methods do not pay sufficient attention to the geometric properties of data. Geometric algebra (GA) is a generalization of complex numbers and quaternions able to describe spatial objects and the relations between them. This paper uses conformal GA (CGA), which is a part of GA, to transform a vector in a real vector space into a vector in a CGA space and presents a proposed new clustering method using conformal vectors. In particular, this paper shows that the proposed method was able to extract the geometric clusters which could not be detected by conventional methods.
2011 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2011), June 27-30, 2011, Grand Hyatt Taipei, Taipei, Taiwan
journal article
IEEE
2011-06
application/pdf
2011 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)
2540
2545
http://dx.doi.org/10.1109/FUZZY.2011.6007574
http://hdl.handle.net/2237/20706
1098-7584
https://nagoya.repo.nii.ac.jp/record/18609/files/5_A_Clustering_Method_for_Geometric_Data.pdf
eng
https://doi.org/10.1109/FUZZY.2011.6007574
978-1-4244-7315-1
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