@article{MTMT:35775630,
	title = {A Parametric Design Method for Landscape Garden Layout Based on Clustering Algorithm},
	url = {https://m2.mtmt.hu/api/publication/35775630},
	author = {Lin, Z.},
	doi = {10.1142/S0129156425402645},
	journal-iso = {IJHSES},
	journal = {INTERNATIONAL JOURNAL OF HIGH SPEED ELECTRONICS AND SYSTEMS},
	volume = {34},
	unique-id = {35775630},
	issn = {0129-1564},
	year = {2025},
	eissn = {1793-6438}
}

@inproceedings{MTMT:35415092,
	title = {AI-Facilitated Dynamic Threshold-Tuning for a Maritime Domain Awareness Module},
	url = {https://m2.mtmt.hu/api/publication/35415092},
	author = {Chan, S.},
	booktitle = {2024 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT)},
	doi = {10.1109/IAICT62357.2024.10617473},
	unique-id = {35415092},
	year = {2024},
	pages = {192-198}
}

@article{MTMT:35183988,
	title = {A Resource Sharing Method of Higher Vocational Distance Online Education Based on Sparse Clustering Algorithm},
	url = {https://m2.mtmt.hu/api/publication/35183988},
	author = {Han, X. and Wang, X.},
	doi = {10.1007/978-3-031-51471-5_6},
	journal-iso = {LECT NOTES INST COMPUT SCI SOC INF TELECOMMUN ENG},
	journal = {LECTURE NOTES OF THE INSTITUTE FOR COMPUTER SCIENCES SOCIAL-INFORMATICS AND TELECOMMUNICATIONS ENGINEERING},
	volume = {545},
	unique-id = {35183988},
	issn = {1867-8211},
	year = {2024},
	eissn = {1867-822X},
	pages = {85-101}
}

@article{MTMT:34655692,
	title = {Statistical method for clustering high-dimensional data based on fuzzy mathematical modeling},
	url = {https://m2.mtmt.hu/api/publication/34655692},
	author = {Wang, C.},
	doi = {10.2478/amns.2023.2.01452},
	journal-iso = {APPL MATH NONLIN SCI},
	journal = {APPLIED MATHEMATICS AND NONLINEAR SCIENCES},
	volume = {9},
	unique-id = {34655692},
	year = {2024},
	eissn = {2444-8656}
}

@article{MTMT:35141484,
	title = {Clustering statistical method of high dimensional sparse data based on fuzzy data},
	url = {https://m2.mtmt.hu/api/publication/35141484},
	author = {Wang, Hu},
	doi = {10.1088/1742-6596/2791/1/012060},
	journal-iso = {J PHYS CONF SER},
	journal = {JOURNAL OF PHYSICS-CONFERENCE SERIES},
	volume = {2791},
	unique-id = {35141484},
	issn = {1742-6588},
	abstract = {Developing effective clustering and statistical methods for high-dimensional sparse data presents unique challenges compared to traditional low-dimensional data. To address this, a novel approach is proposed, leveraging fuzzy data principles to enhance the clustering and statistical performance of high-dimensional sparse datasets. The method builds upon the fuzzy C-means clustering algorithm, introducing key modifications for better suitability to high-dimensional sparse data. One crucial enhancement involves tackling the local optimization problem by optimizing the initial clustering center, significantly reducing clustering statistical time. Replacing the original Euclidean distance with cosine distance improves the clustering and statistical performance of high-dimensional sparse data. Experimental results have shown that this method has superior clustering statistical performance when the data dimensions are different. When the data dimension is low, and the blocking ratio is 10%, the clustering statistical effect is optimal. When the data dimension is high, and the blocking ratio is 40%, the clustering statistical effect is optimal. This method has higher hit rates and clustering statistical efficiency at different sparsity levels.},
	year = {2024},
	eissn = {1742-6596}
}

@article{MTMT:33753494,
	title = {Hálózatalapú modell- és adatredukciós módszer},
	url = {https://m2.mtmt.hu/api/publication/33753494},
	author = {Kosztyán, Zsolt Tibor},
	doi = {10.20311/stat2023.04.hu0289},
	journal-iso = {STATISZTIKAI SZEMLE},
	journal = {STATISZTIKAI SZEMLE},
	volume = {101},
	unique-id = {33753494},
	issn = {0039-0690},
	abstract = {A hálózatelemzés új távlatokat nyit az adatelemzés területén. Az adatpontokat csomópontokként és a közöttük lévő kapcsolatokat élekként ábrázolva „adathálózatot” kapunk, amellyel megnyílik a lehetőség az exponenciálisan fejlődő hálózatos elemzés eszköztárának alkalmazására is. Tanulmányomban egy új, hálózatalapú modell- és adatredukciós módszer létrehozását javaslom, amely egy olyan, nem paraméteres eljárás, amely modellredukció esetében megadja a látens változók, adatredukció esetében pedig a klasztercentrumok számát. A kialakított módszer robusztus, mivel képes kevés megfigyelés alapján is meghatározni a változócsoportokat, illetve kevés változó alapján az adatcsoportokat. A javasolt módszer alkalmazható szimmetrikus és aszimmetrikus változó- és adat-távolságmértékek esetén is. A módszert szimulált és valós adatokon is tesztelem. Az elkészült módszer R-programnyelvben validált csomagként is elérhető.},
	year = {2023},
	pages = {289-324},
	orcid-numbers = {Kosztyán, Zsolt Tibor/0000-0001-7345-8336}
}

@mastersthesis{MTMT:34136169,
	title = {A Robust Unified Graph Model Based on Molecular Data Binning for Subtype Discovery in High-dimensional Spaces},
	url = {https://m2.mtmt.hu/api/publication/34136169},
	author = {Muhammad, Sadiq Hassan Zada},
	unique-id = {34136169},
	year = {2023}
}

@article{MTMT:33282019,
	title = {Two stages biclustering with three populations},
	url = {https://m2.mtmt.hu/api/publication/33282019},
	author = {Sun, J. and Huang, Q.},
	doi = {10.1016/j.bspc.2022.104182},
	journal-iso = {BIOMED SIGNAL PROCES},
	journal = {BIOMEDICAL SIGNAL PROCESSING AND CONTROL},
	volume = {79},
	unique-id = {33282019},
	issn = {1746-8094},
	year = {2023},
	eissn = {1746-8108}
}

@article{MTMT:32789051,
	title = {A New Meta-Heuristics Data Clustering Algorithm Based on Tabu Search and Adaptive Search Memory},
	url = {https://m2.mtmt.hu/api/publication/32789051},
	author = {Alotaibi, Y.},
	doi = {10.3390/sym14030623},
	journal-iso = {SYMMETRY-BASEL},
	journal = {SYMMETRY (BASEL)},
	volume = {14},
	unique-id = {32789051},
	year = {2022},
	eissn = {2073-8994}
}

@article{MTMT:33198345,
	title = {A unified graph model based on molecular data binning for disease subtyping},
	url = {https://m2.mtmt.hu/api/publication/33198345},
	author = {Hassan, Zada M.S. and Yuan, B. and Khan, W.A. and Anjum, A. and Reiff-Marganiec, S. and Saleem, R.},
	doi = {10.1016/j.jbi.2022.104187},
	journal-iso = {J BIOMED INFORM},
	journal = {JOURNAL OF BIOMEDICAL INFORMATICS},
	volume = {134},
	unique-id = {33198345},
	issn = {1532-0464},
	year = {2022},
	eissn = {1532-0480}
}