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Performance Analysis and Evaluation of Clustering Algorithms using WEKA

Author

Listed:
  • Shital Patel
  • Pooja Pancholi
  • Arpita Chaudhury

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.

Suggested Citation

  • Shital Patel & Pooja Pancholi & Arpita Chaudhury, 2024. "Performance Analysis and Evaluation of Clustering Algorithms using WEKA," International Journal of Scientific Research in Computer Science, Engineering and Information Technology, International Journal of Scientific Research in Computer Science, Engineering and Information Technology, vol. 10(2), pages 677-684, April.
    • Handle: RePEc:jbh:ijsrcs:v10:y2024:i2:id:128
    DOI: 10.32628/CSEIT2410251
    Note: Article URL: https://ijsrcseit.com/home/article/view/CSEIT2410251
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