Hybrid Density- Grid Based Clustering Algorithms: A Review
Sakshi Nalawade, Sannidhi Gokhale, Sakshi Ingale, Sandhya Arora, Sunita A. Jahirabadkar · 2023
One of the foundational data mining operations called ‘Clustering’ finds similar objects in a given dataset. The paper explores the study of density-based and grid-based clustering algorithms, as well as a combination of both, to identify groups of similar objects that are dissimilar from other objects in different sets of clusters. We performed in detail study of different algorithms in above mentioned categories. Furthermore, the systematic exploration and survey of grid-based clustering algorithms help in comprehending their diverse benefits. This article provides a foundation for identifying the distinctive features, strengths, and potential applications of density-based and grid-based clustering algorithms, thereby facilitating the development of a novel clustering algorithm through their integration. Additionally, we observed the advantages and disadvantages of these algorithms to understand their evolution and applications. This survey allowed us to contemplate the limitations of density-based clustering algorithms. Moreover, conducting a survey of grid-based clustering algorithms helps us understand their various benefits. These approaches can be combined in order to develop a novel clustering algorithm.