Agglomerative

Agglomerative clustering is a hierarchical clustering technique widely used in unsupervised machine learning. It operates on the principle of merging individual data points into larger clusters based on their similarity. Initially, each data point is treated as a separate cluster. The algorithm then iteratively combines the two closest clusters until only one cluster remains or a specified number of clusters is achieved. This bottom-up approach allows for the creation of a dendrogram, a tree-like structure that visually represents the merging process and the relationships between clusters. Agglomerative clustering is particularly useful for exploratory data analysis and pattern recognition.

Agglomerative Clustering and Dendrograms — Explained

Towards Data Science

Agglomerative Clustering is a type of hierarchical clustering algorithm. It is an unsupervised machine learning technique that divides the population into several clusters such that data points in…

Breaking down the agglomerative clustering process

Towards Data Science

Agglomerative Clustering is one of the most useful methods in the clustering process. Here in this article, I break down how the agglomerative process work

Hierarchical Clustering (Agglomerative)

Analytics Vidhya

In this article we will understand Agglomerative approach to Hierarchical Clustering, Steps of Algorithm and its mathematical approach. Till now we have seen about Clustering, Hierarchical…

Generating and Videolizing Agglomerative Processes

Machine Learning Techniques

This short article explains how to efficiently simulate the evolution of agglomerative processes, and visualize their behavior with data animations. I use a generic, simple model for illustration purp...

Feature agglomeration

Scikit-learn Examples

Feature agglomeration These images how similar features are merged together using feature agglomeration.

Agglomerative clustering with and without structure

Scikit-learn Examples

Agglomerative clustering with and without structure This example shows the effect of imposing a connectivity graph to capture local structure in the data. The graph is simply the graph of 20 nearest n...

Agglomerative clustering with different metrics

Scikit-learn Examples

Agglomerative clustering with different metrics Demonstrates the effect of different metrics on the hierarchical clustering. The example is engineered to show the effect of the choice of different met...

Various Agglomerative Clustering on a 2D embedding of digits

Scikit-learn Examples

Various Agglomerative Clustering on a 2D embedding of digits An illustration of various linkage option for agglomerative clustering on a 2D embedding of the digits dataset. The goal of this example is...

Scaling Agglomerative Clustering for Big Data: an Introduction to RAC

Towards Data Science

Scaling Agglomerative Clustering for Big Data Learn how to use Reciprocal Agglomerative Clustering (RAC) to power hierarchical clustering of large datasets Photo by Nastya Dulhiier on Unsplash. Intro...

Everything to know about Hierarchical Clustering, Agglomerative Clustering & Divisive Clustering

Towards AI

Hierarchical Clustering Continue reading on Towards AI

Automatic Dendrogram Cut

Towards Data Science

Clustering analysis is a useful technique to explore structures amidst data. Among lots of algorithms, agglomerative clustering is one simple yet useful method that builds hierarchy of clusters from…

Aggregation and Grouping

Towards Data Science

This is the first implementation of aggregation and grouping in a Pharo DataFrame. It covers only the basic functionality, such as grouping a data frame or a series by the values of another series of…...