Data Mining Clustering Techniques: A Comprehensive Overview and Critical Constraints

Description

Page: 41-46

Amit Khirbat and Sandeep (Department of Computer Science & Engineering, Om Sterling Global University, Hisar, Haryana)

Data mining, also known as knowledge discovery in databases (KDD), is the process of extracting useful patterns and insights from vast amounts of data. However, the effectiveness of data mining depends on several factors, such as the quality of the data, the techniques chosen, and the amount of processing power available. Data mining requires the use of clustering algorithms. It has long been an important analytical tool for organizing unlabeled data for more meaningful insights. The inherent complexity of clustering problems has inspired the development of several clustering methods. Data clustering presents unique challenges, and these techniques are tailored to meet those challenges. There is no agreement on which of the numerous suggested classification methods is better suited to any particular dataset. This highlights the significance of thoroughly comparing approaches in a wide range of contexts. Clustering approaches in data mining are examined in detail in this work, along with the difficulties associated with them.