Published Paper Details:

Data clustering primarily serves as a solution for tackling unsupervised learning challenges and represents a fundamental tool applied across various domains-including data mining, pattern recognition, and artificial intelligence. The main objective of data clustering is to group similar objects and allocate them to different categories. Different clustering techniques are developed and implemented to categorize data objects. Partitioning and hierarchical techniques [1] are the two classified categories of data clustering. Additionally, alternative methods such as grid-based, density-based, and fuzzy C-mean clustering approaches are also available. This paper's objective is to provide a comprehensive overview of data clustering. It covers the historical context of this technique, furnishes a precise definition, and thoroughly explores various types of clustering methods while critically assessing their respective strengths and weaknesses. Furthermore, the paper delves into the practical applications of data clustering and highlights recently developed algorithms, facilitating a meaningful comparison among different clustering approaches. In summary, this paper offers a concise yet thorough review of the entire spectrum of whole data clustering method