Performance Analysis and Evaluation of  Clustering Algorithms using WEKA

Shital Patel; Pooja Pancholi; Arpita Chaudhury

doi:10.32628/CSEIT2410251

Authors

Shital Patel Department of Computer Science, Ganpat University, Mehsana, Gujarat, India Author
Pooja Pancholi Department of Computer Science, Ganpat University, Mehsana, Gujarat, India Author
Arpita Chaudhury Department of Computer Science, Ganpat University, Mehsana, Gujarat, India Author

DOI:

https://doi.org/10.32628/CSEIT2410251

Keywords:

K-Means Clustering, Hierarchical Clustering, Density Based Clustering, EM Algorithm, Canopy Algorithm, WEKA tool

Abstract

Clustering, an unsupervised learning technique, to find inherent groupings in un-labelled data. It seems to be referring to a study or research paper that examines and uses a number of clustering algorithms, including the canopy method, k-Means clustering, hierarchical clustering, density-based clustering, and EM algorithm. WEKA, a clustering program, is used for the examination of these techniques. and the effectiveness of these algorithms is evaluated through experiments using social network Ads datasets. The goal of this research paper or study seems to be to assess how well these clustering algorithms perform in grouping data within social network Ads datasets. Such analyses can help identify the most suitable algorithm for a specific type of data or problem domain and may lead to insights into the underlying structure of the data.

Downloads

Download data is not yet available.

References

D Swasti Singhal,” A Study on WEKA Tool for Data Preprocessing, Classification and Clustering”, International Journal of Innovative Technology and Exploring Engineering (IJITEE) ,Vol. 2(6), May 2013.

Team, E. "What is machine learning? a definition." online].(https://www. expertsystem. com/machine-learning-definition/ (2019).

Fung, Glenn. "A comprehensive overview of basic clustering algorithms." (2001): 1-37.

Namratha, M., and T. R. Prajwala. "A comprehensive overview of clustering algorithms in pattern recognition." IOSR Journal of Computer Engineering 4.6 (2012): 23-30. DOI: https://doi.org/10.9790/0661-0462330

N Patel, Meghna, Shital Patel, and Sonal Patel. "Data Analysis in Shopping Mall data using K-Means Clustering." 2022 4th International Conference on Advances in Computing, Communication Control and Networking (ICAC3N). IEEE, 2022. DOI: https://doi.org/10.1109/ICAC3N56670.2022.10074249

Devi, R. Delshi Howsalya, and P. Deepika. "Performance comparison of various clustering techniques for diagnosis of breast cancer." 2015 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC). IEEE, 2015. DOI: https://doi.org/10.1109/ICCIC.2015.7435711

Chitra, K., and D. Maheswari. "A comparative study of various clustering algorithms in data mining." International Journal of Computer Science and Mobile Computing 6.8 (2017): 109-115.

Kameshwaran, K., and K. Malarvizhi. "Survey on clustering techniques in data mining." International Journal of Computer Science and Information Technologies 5.2 (2014): 2272-2276.

Yadav, Krishna Mohan, et al. "Clustering Techniques and Algorithms of Data Mining–A Review."

Surya Narayana, G., and D. Vasumathi. "An attributes similarity-based K-medoids clustering technique in data mining." Arabian Journal for Science and Engineering 43.8 (2018): 3979-3992. DOI: https://doi.org/10.1007/s13369-017-2761-2

Wegmann, Marc, et al. "A review of systematic selection of clustering algorithms and their evaluation." arXiv preprint arXiv:2106.12792 (2021).

Popat, S. K., & Emmanuel, M. (2014). Review and comparative study of clustering techniques. International journal of computer science and information technologies, 5(1), 805-812.