GUVI Blogs

Most clustering methods force you to answer their biggest question up front: how many clusters do you want? K‑Means, for example, needs that number before it starts, and a wrong choice can make the results meaningless. That requirement often defeats the point of clustering, which is to discover the group structure in unfamiliar data.

Affinity Propagation avoids this...

Agglomerative clustering is a hierarchical clustering method in machine learning that identifies hidden patterns in unlabeled datasets. Unlike K-Means clustering, it follows a bottom-up approach in which each data point starts as its own cluster before merging into larger groups.

Using sklearn’s Agglomerative Clustering and scipy dendrogram makes hierarchical cluste...

Clustering is one of the key techniques in unsupervised machine learning for grouping similar data points. Many beginners start with K-Means clustering, but hierarchical methods offer a more organized way to analyze data relationships.

One such method is divisive clustering.

Divisive clustering uses a top-down strategy. The whole dataset starts as one clus...

One of the most persistent challenges in clustering is also one of the most fundamental: how many clusters does the data actually contain?

Most clustering algorithms, K-means, Gaussian Mixture Models, and spectral clustering, require the practitioner to specify the number of clusters K before fitting begins. In practice, K is rarely known in advance. It must be gues...

Many of the most interesting machine learning problems involve data that is incomplete, ambiguous, or grouped into hidden categories we cannot directly observe. Customer purchase records do not come labelled with personality segments. Medical measurements do not arrive tagged with underlying biological subtypes. Astronomy datasets do not specify which star cluster each observ...