Study of Clustering Data Mining Techniques

Authors

  • Dinesh Bhardwaj and Dr. Sonawane Vijay Ramnath

Keywords:

Clustering, Data mining, Algorithms, Machine learning, Data.

Abstract

Data mining's primary goal is to take a massive data collection and break it down into a more manageable shape for analysis and application. Exploratory data analysis and data mining applications often center on clustering. The term "clustering" refers to the process of categorizing data points into groupings where the items within each cluster have more similarities than differences (clusters). Each technique serves a unique purpose, determined by the nature of the data at hand and the demands of the application. Nonetheless, our research has led us to the conclusion that the K-means technique outperforms the alternatives in a wide variety of settings. In this study, senior undergraduate and master's degree students from the Faculty of Economics and Business Administration participated through the use of questionnaires in a collaborative effort, with the collected data being processed through data mining clustering techniques, graphical and percentage representations, using algorithms implemented in the software Weka.

Published

2023-03-20

How to Cite

Dinesh Bhardwaj and Dr. Sonawane Vijay Ramnath. (2023). Study of Clustering Data Mining Techniques. SJIS-P, 35(1), 440–445. Retrieved from http://sjis.scandinavian-iris.org/index.php/sjis/article/view/320

Issue

Section

Articles