• Urvashi Soni

Clustering is a type of unsupervised learning that seeks to uncover the natural groupings of a set of patterns, points, or objects. A fundamental issue in clustering algorithms is the lack of a deterministic technique based on which users may pick which clustering method best matches a particular set of input data. This is because particular optimization criteria are used. Clustering ensemble as knowledge reuse provides a solution to the clustering difficulties. It tries to investigate high stability and robustness findings by assembling computed solutions obtained by base clustering algorithms without access to the features. When low-quality ensemble members are used, combining base clustering’s reduces the quality of the final solution. Several academics in this field have proposed the idea of clustering ensemble selection to pick a subset of base clustering based on quality and variety. While the clustering ensemble combines all ensemble members, the clustering ensemble selection selects a subset of those members to build a smaller cluster ensemble that outperforms the clustering ensemble. This examination covers the history of data clustering, as well as an overview of basic clustering techniques, clustering ensemble algorithms, including ensemble generation mechanisms and consensus functions, and clustering ensemble selection strategies that take quality and variety into account.

"Clustering Ensemble: An Empirical Study", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 10, page no.c795-c802, October-2022, Available :http://www.jetir.org/papers/JETIR2210305.pdf

"Clustering Ensemble: An Empirical Study", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 10, page no. ppc795-c802, October-2022, Available at : http://www.jetir.org/papers/JETIR2210305.pdf