Large dataset partitioning using ensemble partition-based clustering with majority voting technique

<span lang="EN-US">Large datasets have become useful in data mining for processing, storing, and handling vast amounts of data. However, processing large is time-consuming memory intensive. As a result, the researchers adopted partitioning strategy to improve controllability performance reduce time required handle datasets. Unfortunately, numerous clustering techniques available literature could confuse experts choosing best given dataset. Furthermore, no technique can tackle all problems, such as cluster structure, noise, or density. To manage datasets, existing need scalable solutions. Therefore, this paper proposes an ensemble partition-based with majority voting dataset using aggregation k-means, k-medoids, fuzzy c-means, expectation-maximization (EM) density-based spatial applications noise (DBSCAN) techniques. These individually first stage. The final clusters are discovered next stage through among five algorithms. algorithms assigned instances most votes. experimental findings demonstrate that method surpasses other terms execution accuracy.</span>