Comparative Evaluation of Clustering Methods in Working With Big Data
- Авторлар: Panferova E.V.1, Matushin R.A.1
-
Мекемелер:
- Tula State Lev Tolstoy Pedagogical University, Institute of Advanced Information Technologies
- Шығарылым: № 2 (65) (2024)
- Беттер: 61-67
- Бөлім: Computer science
- URL: https://journal-vniispk.ru/1993-0550/article/view/307277
- DOI: https://doi.org/10.17072/1993-0550-2024-2-61-67
- ID: 307277
Дәйексөз келтіру
Толық мәтін
Аннотация
The paper considers the problems of using cluster analysis methods in the tasks of processing, analyzing and storing structured and unstructured large-volume data and evaluates the feasibility of their use in various aspects of working with Big Data. The aim of the work is to identify the most preferred of the common data clustering algorithms. To do this, the task was set to conduct a comparative evaluation of the following popular algorithms: hierarchical clustering, k-means, DBSCAN, OPTICS and CURE. The algorithmic complexity of the methods is considered, the stability of algorithms to noise and emissions is analyzed, as well as the potential possibilities of visualizing their results and the scope of economic application are indicated. Conclusions are drawn about the advantages and disadvantages of each presented algorithm when used in the field of Big Data and about the most preferred methods of cluster analysis in various aspects of working with big data.
Негізгі сөздер
Авторлар туралы
E. Panferova
Tula State Lev Tolstoy Pedagogical University, Institute of Advanced Information Technologies
Хат алмасуға жауапты Автор.
Email: gamma15@inbox.ru
Candidate of Technical Sciences, Associate Professor 125, Lenin Ave., Tula, Russia, 300026
R. Matushin
Tula State Lev Tolstoy Pedagogical University, Institute of Advanced Information Technologies
Email: roman.matyuschin2017@yandex.ru
Master’s Student 125, Lenin Ave., Tula, Russia, 300026
Әдебиет тізімі
- Goodfellow, I., Bengio, Y. and Courville, A. (2016). Deep Learning, Adaptive Computa-tion and Machine Learning series, the MIT Press.
- Danilenko, A.N. (2018), Struktury dannykh i analiz slozhnosti algoritmov [Data structures and algorithm complexity analysis], № 1272, Samara University Press, Samara, Russia.
- Jain, A. K., Murty, M. N. and Flynn, P. J. (1999), "Data clustering: a review", ACM Computing Surveys, vol. 31, no. 3, pp. 264-323.
- ScikitLearn (2024), "K-means", available at: https://scikit-learn.org/stable/modules/clustering.
- html#k-means (Accessed 03 April 2024).
- Ester M., Kriegel Hans H.-P., Sander J. and Xu X. (1996), "A density-based algorithm for discovering clusters in large spatial databases with noise", Proceedings KDD'96, vol. 34, pp. 226-231.
- Feng L., Liu K., Tang F. and Meng Q. (2017), "GO-DBSCAN: Improvements of DBSCAN Algorithm Based on Grid", vol. 9, no. 3, pp. 151.
- Ankerst M., Breunig M. M., Kröger P. and Sander J. (1999), "OPTICS: ordering points to identify the clustering structure", Proceedings SIGMOD '99, vol. 2, pp. 49-60.
- Han J., Kamber M. and Pei J., (2012), "Data mining: Concepts and Techniques", Morgan Kaufmann Series, Waltham, USA.
- Geeksforgeeks (2021), "Basic Understanding of CURE Algorithm", available at: https: //www.geeksforgeeks.org/basicunder-standing -of-cure-algorithm/ (Accessed 03 April 2024).
- Guha, S., Rastogi, R. and Kyuseok, S., (1998), "CURE: An Efficient Clustering Algorithm for Large Databases", ACM SIGMOD Conference, vol. 27, no. 2, pp. 73-84.
- Сartetika (2023), "Clustering of spatial data – density algorithms and DBCSAN", available at: https://cartetika.ru/tpost/k05o2ndpf1-klasteri-zatsiya-prostranstvennih-dannih (Accessed 11 April 2024).
- Deepgram (2024), "CURE Algorithm", avai-lable at: https://deepgram.com/ai-glossary /cure-algorithm (Accessed 11 April 2024).
Қосымша файлдар
