Clustering with H2O
Learn the fundamentals of clustering and its implementation using the H2O package.
Introduction to clustering models
Unsupervised clustering models group similar data points based on their inherent patterns or similarities. Their goal is to find natural groupings within the data without any predefined labels or categories.
To achieve well-defined clusters, the algorithm measures the similarity or distance between data points and groups that are close together. The goal of unsupervised clustering is to maximize intracluster similarity while maximizing intercluster dissimilarity. In other words, we want the data points within a cluster to be similar to each other, and at the same time, we want the clusters to be distinct from other clusters.
By optimizing these two factors, unsupervised clustering algorithms can identify meaningful and well-separated clusters in the data. They allow us to uncover underlying patterns, segment the data into distinct groups, and gain insights into the structure of the dataset.