Clustering introduction, Hierarchical Algorithms

Clustering is a technique used in data mining to group similar objects or data points together based on their characteristics. It is a type of unsupervised learning, meaning that it is used to find patterns in data without being provided with any prior knowledge of the data structure or any information about the groups.

The goal of clustering is to partition a set of objects into groups, or clusters, based on their similarity or distance from each other. The similarity or distance between objects is measured using a distance function, which can be either Euclidean distance or some other distance measure.

There are several clustering algorithms that can be used to cluster data. Hierarchical clustering algorithms are one of the most popular and widely used clustering techniques. They can be divided into two categories: agglomerative and divisive.

Agglomerative hierarchical clustering starts by treating each data point as a separate cluster and then recursively merges the most similar clusters until a single cluster containing all the data points is formed. Divisive hierarchical clustering starts with a single cluster containing all the data points and then recursively splits it into smaller clusters until each cluster contains only one data point.

The output of hierarchical clustering is a dendrogram, which is a tree-like diagram that shows the hierarchical relationships between clusters. The dendrogram can be cut at a certain level to obtain a specific number of clusters, based on the researcher’s requirements.

Hierarchical clustering algorithms have several advantages, including their ability to handle large datasets and their ability to generate a tree-like structure that can be easily interpreted. However, they can be computationally expensive and may not be suitable for very large datasets.

Overall, clustering is a useful technique in data mining for grouping similar objects together based on their characteristics. Hierarchical clustering algorithms, in particular, are a popular and effective way of clustering data, especially for smaller datasets.