A Survey on Performance Improvement of Data Analysis Using Unsupervised K-Means Clustering

  • Arpana Kumari Bhabha Engineering Research Institute, MP, Bhopal, India
  • Monika Raghuvanshi Bhabha Engineering Research Institute, MP, Bhopal, India
Keywords: Data Mining, Unsupervised Machine Learning Algorithms, Clustering Method, K-Means Clustering, Dataset, Data Analysis


The algorithms clustering implemented on the machines and made intelligent machines are called unsupervised machine learning algorithms. They can perform essential tasks by k-means clustering algorithm based on improved quantum particle swarm optimization algorithm is often more error in data analysis. As more data becomes available, more complex problems can be tackled and solved. The analysis of patient's data is becoming more critical to evaluate the patient's medical condition and prevent and take precautions for the future. With the help of technology and computerized automation of machines, data can be analyzed more efficiently. Managing the massive volume of data has many problems interrelated to data security. Experiments on actual datasets show that our technique will get similar results with standard ways with fewer computation tasks. Process mining and data mining techniques have opened new access for the diagnosis of disease.

Similarly, data mining can provide effective treatment for a disease's triennial prevention; finally, an effective clustering result is obtained. The algorithm is tested with the UCI data set. The results show that the improved algorithm ensures the global convergence of the algorithm and brings more accurate clustering results.


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How to Cite
Kumari, A., & Raghuvanshi, M. (2021). A Survey on Performance Improvement of Data Analysis Using Unsupervised K-Means Clustering. International Journal of Advanced Computer Technology, 10(5), 7-10. Retrieved from https://ijact.org/index.php/ijact/article/view/96