Adaptive type2-possibilistic C-means clustering and its application to microarray datasets-Reference-Cited by-同舟云学术

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Adaptive type2-possibilistic C-means clustering and its application to microarray datasets

Published:2023-03-10 Issue:10 Volume:56 Page:11017-11052
ISSN:0269-2821
Container-title:Artificial Intelligence Review
language:en
Short-container-title:Artif Intell Rev

Author:

Moattar Husseini Zohre,Fazel Zarandi Mohammad Hossein,Ahmadi Abbas

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Linguistics and Language,Language and Linguistics

Link

https://link.springer.com/content/pdf/10.1007/s10462-022-10380-5.pdf

Reference78 articles.

1. Acharya S, Saha S, Sahoo P (2019) Bi-clustering of microarray data using a symmetry-based multi-objective optimization framework. Soft Comput 23:5693–5714. https://doi.org/10.1007/s00500-018-3227-5

2. Alashwal H, El Halaby M, Crouse JJ et al (2019) The application of unsupervised clustering methods to Alzheimer’s disease. Front Comput Neurosci 13:1–9. https://doi.org/10.3389/fncom.2019.00031

3. Almardeny Y, Boujnah N, Cleary F (2020) A novel outlier detection method for multivariate data. IEEE Trans Knowl Data Eng

4. Amidan BG, Ferryman TA, Cooley SK (2005) Data outlier detection using the chebyshev theorem. IEEE Aerosp Conf Proc 2005:3–8. https://doi.org/10.1109/AERO.2005.1559688

5. Askari S, Montazerin N, Fazel Zarandi MH (2017) Generalized possibilistic Fuzzy C-means with novel cluster validity indices for clustering noisy data. Appl Soft Comput J 53:262–283. https://doi.org/10.1016/j.asoc.2016.12.049

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2. Interval type-2 fuzzy approach for retinopathy detection in fundus images;International Journal of Knowledge-based and Intelligent Engineering Systems;2024-05-16

3. Type2 soft biclustering framework for Alzheimer microarray;Applied Soft Computing;2024-02

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