146 related articles for article (PubMed ID: 35732967)
1. Unsupervised learning methods for efficient geographic clustering and identification of disease disparities with applications to county-level colorectal cancer incidence in California.
McMahon ME; Doroshenko L; Roostaei J; Cho H; Haider MA
Health Care Manag Sci; 2022 Dec; 25(4):574-589. PubMed ID: 35732967
[TBL] [Abstract][Full Text] [Related]
2. Identifying and evaluating clinical subtypes of Alzheimer's disease in care electronic health records using unsupervised machine learning.
Alexander N; Alexander DC; Barkhof F; Denaxas S
BMC Med Inform Decis Mak; 2021 Dec; 21(1):343. PubMed ID: 34879829
[TBL] [Abstract][Full Text] [Related]
3. Sheep's coping style can be identified by unsupervised machine learning from unlabeled data.
Çakmakçı C
Behav Processes; 2022 Jan; 194():104559. PubMed ID: 34838901
[TBL] [Abstract][Full Text] [Related]
4. Comparison of machine learning clustering algorithms for detecting heterogeneity of treatment effect in acute respiratory distress syndrome: A secondary analysis of three randomised controlled trials.
Sinha P; Spicer A; Delucchi KL; McAuley DF; Calfee CS; Churpek MM
EBioMedicine; 2021 Dec; 74():103697. PubMed ID: 34861492
[TBL] [Abstract][Full Text] [Related]
5. An unsupervised neuromorphic clustering algorithm.
Diamond A; Schmuker M; Nowotny T
Biol Cybern; 2019 Aug; 113(4):423-437. PubMed ID: 30944983
[TBL] [Abstract][Full Text] [Related]
6. An analysis framework for clustering algorithm selection with applications to spectroscopy.
Crase S; Thennadil SN
PLoS One; 2022; 17(3):e0266369. PubMed ID: 35358292
[TBL] [Abstract][Full Text] [Related]
7. Machine-learned cluster identification in high-dimensional data.
Ultsch A; Lötsch J
J Biomed Inform; 2017 Feb; 66():95-104. PubMed ID: 28040499
[TBL] [Abstract][Full Text] [Related]
8. A density-based competitive data stream clustering network with self-adaptive distance metric.
Xu B; Shen F; Zhao J
Neural Netw; 2019 Feb; 110():141-158. PubMed ID: 30557793
[TBL] [Abstract][Full Text] [Related]
9. Voxel-based clustered imaging by multiparameter diffusion tensor images for glioma grading.
Inano R; Oishi N; Kunieda T; Arakawa Y; Yamao Y; Shibata S; Kikuchi T; Fukuyama H; Miyamoto S
Neuroimage Clin; 2014; 5():396-407. PubMed ID: 25180159
[TBL] [Abstract][Full Text] [Related]
10. Application of Multiple Unsupervised Models to Validate Clusters Robustness in Characterizing Smallholder Dairy Farmers.
Nyambo DG; Luhanga ET; Yonah ZO; Mujibi FDN
ScientificWorldJournal; 2019; 2019():1020521. PubMed ID: 30718979
[TBL] [Abstract][Full Text] [Related]
11. Using unsupervised learning to classify inlet water for more stable design of water reuse in industrial parks.
Chen K; Shi X; Zhang Z; Chen S; Ma J; Zheng T; Alfonso L
Water Sci Technol; 2024 Apr; 89(7):1757-1770. PubMed ID: 38619901
[TBL] [Abstract][Full Text] [Related]
12. Distributed dual vigilance fuzzy adaptive resonance theory learns online, retrieves arbitrarily-shaped clusters, and mitigates order dependence.
Brito da Silva LE; Elnabarawy I; Wunsch DC
Neural Netw; 2020 Jan; 121():208-228. PubMed ID: 31574412
[TBL] [Abstract][Full Text] [Related]
13. Subspace K-means clustering.
Timmerman ME; Ceulemans E; De Roover K; Van Leeuwen K
Behav Res Methods; 2013 Dec; 45(4):1011-23. PubMed ID: 23526258
[TBL] [Abstract][Full Text] [Related]
14. Does Determination of Initial Cluster Centroids Improve the Performance of
Pourahmad S; Basirat A; Rahimi A; Doostfatemeh M
Comput Math Methods Med; 2020; 2020():7636857. PubMed ID: 32802153
[TBL] [Abstract][Full Text] [Related]
15. Feature selection for unsupervised machine learning of accelerometer data physical activity clusters - A systematic review.
Jones PJ; Catt M; Davies MJ; Edwardson CL; Mirkes EM; Khunti K; Yates T; Rowlands AV
Gait Posture; 2021 Oct; 90():120-128. PubMed ID: 34438293
[TBL] [Abstract][Full Text] [Related]
16. An unsupervised hierarchical dynamic self-organizing approach to cancer class discovery and marker gene identification in microarray data.
Hsu AL; Tang SL; Halgamuge SK
Bioinformatics; 2003 Nov; 19(16):2131-40. PubMed ID: 14594719
[TBL] [Abstract][Full Text] [Related]
17. How Socio-economic Inequalities Cluster People with Diabetes in Malaysia: Geographic Evaluation of Area Disparities Using a Non-parameterized Unsupervised Learning Method.
Ganasegeran K; Abdul Manaf MR; Safian N; Waller LA; Mustapha FI; Abdul Maulud KN; Mohd Rizal MF
J Epidemiol Glob Health; 2024 Mar; 14(1):169-183. PubMed ID: 38315406
[TBL] [Abstract][Full Text] [Related]
18. The novel hierarchical clustering approach using self-organizing map with optimum dimension selection.
Tripathi K
Health Care Sci; 2024 Apr; 3(2):88-100. PubMed ID: 38939618
[TBL] [Abstract][Full Text] [Related]
19. Analysis of Cattle Social Transitional Behaviour: Attraction and Repulsion.
Xu H; Li S; Lee C; Ni W; Abbott D; Johnson M; Lea JM; Yuan J; Campbell DLM
Sensors (Basel); 2020 Sep; 20(18):. PubMed ID: 32961892
[TBL] [Abstract][Full Text] [Related]
20. Late detection of breast and colorectal cancer in Minnesota counties: an application of spatial smoothing and clustering.
Thomas A; Carlin BP
Stat Med; 2003 Jan; 22(1):113-27. PubMed ID: 12486754
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]