A Comparative Evaluation of Similarity Measures for Semi-Supervised Density Peaks Clustering

Hussein, Ahmed Saad

Рақамли технологияларнинг назарий ва амалий масалалари · 2026-yil

Annotatsiya

Semi-supervised Density Peak (SDenPeak) algorithm is known to be efficient and simple in tasks clustering. It improves clustering performance by adding pair-wise constraints, must-link and cannot-link constraints, that drive the grouping process by imposing similarity and dissimilarity between data points. One of the key considerations in clustering accuracy is the selection of similarity measure because various measures reflect diverse structural attributes to data. The problem with the fact that there is no universal best measure of similarity is that it is a tricky task to choose a suitable measure that is dependent on the nature of the data. To explore the effects of the six similarity measures on SDenPeak algorithm performance, the six measures (Euclidean Distance, Cosine Similarity, City Block (Manhattan) Distance, Minkowski Distance, Earth Mover’s Distance (EMD), and Rapid Computation of the Maximal Information Coefficient (RapidMIC) Distance) are evaluated systematically in this study in order to understand their influences Real-world datasets are extensively experimented to evaluate the accuracy of clustering and structural consistency in each of the measures. These findings present comparative information on the effectiveness of the various similarity measures and illustrate their applicability to various data distributions providing a useful guide to achieving the best clustering performance in semi-supervised models.

Maqola ma’lumotlari
MualliflarHussein, Ahmed Saad
JurnalРақамли технологияларнинг назарий ва амалий масалалари
NashriyotТАТУ Самарқанд филиали
ISSN2181-3086, 2181-3094
Nashr sanasi2026-05-14
Jild9
Son2
Betlar7-19
TilIngliz
Ilmiy sohaTexnika
DOI10.62132/ijdt.v9i2.371
Indeksga qo‘shilgan2026-08-26

DOI: 10.62132/ijdt.v9i2.371 · Maqolaning asl sahifasi

Kalit so‘zlar

кластеризация, полуконтролируемое обучение, измерение сходства, пики плотности, реальные наборы данных, clustering, semi-supervised, measurement, density peaks, real-world datasets

Ilmiy soha

Texnika

Iqtibos keltirish

APAHussein, Ahmed Saad (2026). A Comparative Evaluation of Similarity Measures for Semi-Supervised Density Peaks Clustering. Рақамли технологияларнинг назарий ва амалий масалалари, 9(2), 7-19. https://doi.org/10.62132/ijdt.v9i2.371
GOST (OAK)Hussein, Ahmed Saad A Comparative Evaluation of Similarity Measures for Semi-Supervised Density Peaks Clustering // Рақамли технологияларнинг назарий ва амалий масалалари. – 2026. – T. 9, № 2. – P. 7-19. – DOI: 10.62132/ijdt.v9i2.371.
MLAHussein, Ahmed Saad. “A Comparative Evaluation of Similarity Measures for Semi-Supervised Density Peaks Clustering.” Рақамли технологияларнинг назарий ва амалий масалалари, 9(2), 7-19, 2026. https://doi.org/10.62132/ijdt.v9i2.371
ChicagoHussein, Ahmed Saad. “A Comparative Evaluation of Similarity Measures for Semi-Supervised Density Peaks Clustering.” Рақамли технологияларнинг назарий ва амалий масалалари, 9(2), 7-19 (2026). https://doi.org/10.62132/ijdt.v9i2.371
HarvardHussein, Ahmed Saad (2026) ‘A Comparative Evaluation of Similarity Measures for Semi-Supervised Density Peaks Clustering’, Рақамли технологияларнинг назарий ва амалий масалалари, 9(2), 7-19. https://doi.org/10.62132/ijdt.v9i2.371
BibTeX
@article{husseinahmedsaad2026,
  title = {A Comparative Evaluation of Similarity Measures for Semi-Supervised Density Peaks Clustering},
  author = {Hussein, Ahmed Saad},
  journal = {Рақамли технологияларнинг назарий ва амалий масалалари},
  year = {2026},
  volume = {9},
  number = {2},
  pages = {7-19},
  doi = {10.62132/ijdt.v9i2.371},
  url = {https://doi.org/10.62132/ijdt.v9i2.371}
}

Boshqa maqola uchun iqtibos tayyorlash

Рақамли технологияларнинг назарий ва амалий масалалари jurnalidan boshqa maqolalar

Рақамли технологияларнинг назарий ва амалий масалалари — barcha maqolalar · 2026-yil arxivi

Texnika sohasidagi boshqa maqolalar

Texnika sohasidagi OAK jurnallari