Cluster Analysis

Cluster Analysis

Cluster analysis is a technique used in data science and statistics to group similar data points together. It is commonly applied in various fields such as biology, marketing, and social sciences for tasks like customer segmentation, species classification, and identifying patterns in data. Cluster analysis algorithms aim to find meaningful clusters in your data based on similarity or dissimilarity measures....

Hierarchical Clustering

A bottom-up method of clustering is hierarchical clustering. By gradually merging or separating clusters, it builds a hierarchy of clusters. A dendrogram, a structure like a tree, is frequently used to represent the outcome. The dendrogram can be clipped at a given height to produce the required number of clusters....

K-Means Clustering

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Cluster graph on USArrest dataset

An unsupervised non-linear approach called K Means Clustering in R programming organises data based on similarity or similar groups. Specifically, it aims to divide the observations into a predetermined number of clusters. Data is segmented in order to group each training example into a segment known as a cluster. In the unsupervised method, a lot of emphasis is placed on providing raw data while also spending a lot of money on manual review to determine relevance. It is utilised in a number of industries, including banking, healthcare, retail, and media....

Conclusion

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