K-4 Algorithm

A vital tool in unsupervised machine learning is data clustering, which is accomplished by using the K-4 algorithm, sometimes referred to as K-4 clustering. The clustering method involves assembling comparable data items according to their shared attributes. Specifically, K-4 is a variant of the widely recognized K-Means method.
“K” in the K-4 method stands for the number of clusters to be formed. K-4 explicitly seeks to separate the data into four different clusters, in contrast to K-Means, which often focuses on identifying a fixed number of clusters (K). When you have prior knowledge or a particular application that needs precisely four clusters, this can be helpful.

The K-4 algorithm works as follows: first, data points are initially assigned to clusters. Then, to reduce the within-cluster variance, these assignments are iteratively refined. The data are then divided into four clusters as a consequence of this refinement process, which is continued until convergence.

Similar to K-Means, K-4 is a flexible method that has uses in data analysis, image segmentation, and consumer segmentation, among other domains. You must select a suitable value for K (four in this case) depending on the particular issue and dataset that you are dealing with. When used appropriately, the K–4 algorithm can be a potent tool for pattern recognition and data management.