blog writing #unsupervised machine learning

Unsupervised Machine Learning focuses primarily on input vectors that correspond to target values, which is essential in interpreting information based on similarities, patterns, and differences. Therefore, unsupervised machine learning involves using a concise representation of data to generate imaginative content from the data. K-means clustering is an example of unsupervised machine learning that partitions existing datasets into given clusters. This algorithm assigns each data component to one of the existing K clusters, which is similar (Sinaga & Yang, 2020). Clustering aims to reduce the variance between each existing cluster by maximizing the variance between other clusters. K-means Clustering works where the algorithm is initialed by selecting. Then, each data is assigned to a closer centroid using Euclidean distance to form K clusters.