Introduction to Hierarchical Clustering in Python

A hierarchical clustering in python approach bases subsequent cluster definitions on previously established clusters. Dendrograms, a technique used to more effectively arrange data into clusters, show the hierarchical link between the underlying clusters graphically.

Comparing Different Clustering Methods to Hierarchical Clustering.

Although hierarchical clustering is a potent method, there are other types of clustering as well, and each has its own set of benefits and disadvantages.

Let’s see how it contrasts with other clustering techniques, such as K-means and model-based clustering. Although there are many more techniques, hierarchical clustering and these two are the most used and offer a foundation to help understand others. A Python certification course online can teach you the idea.

Hierarchical Clustering vs K Means Clustering.

K-means clustering, in contrast to hierarchical clustering, aims to divide the initial data points into “K” groups or clusters, where “K” is selected by the user.

The general concept is to iteratively combine individuals that are in clusters that minimise the squared Euclidean distance of all the points from the centres across all attributes (variables or features).

Benefits 

• When compared to hierarchical clustering, it is computationally efficient and suitable for the analysis of huge data sets.
• K-means is simpler to comprehend and use.

Drawbacks

• Because the user is forced to predetermine the number of clusters, which may not always be evident, it is less flexible than hierarchical clustering.
• For the same set of data, the outcome is unstable and varies from iteration to iteration.
• Because the use of outliers in the data affects the cluster’s mean, it is more vulnerable to them.
• In addition to being unable to handle categorical data directly, k-means and hierarchical clustering may also perform poorly when dealing with non-continuous or highly variable data.

K-means clustering is still a well-liked technique due to its simplicity and computational effectiveness, despite its drawbacks. It is widely used as a benchmark for assessing how well different clustering approaches function.

Model-based clustering 

A distance matrix is used by K-means and hierarchical clustering methods to represent the separations between each point in the dataset. On the other hand, model-based clustering makes use of statistical methods to locate data clusters. The general procedure is as follows:

• Choose the number of clusters and the statistical model to be used.
• Data was used to fit the model.
• Using the model’s parameters, locate the clusters.

Benefits

• Compared to hierarchical clustering, model-based clustering is more versatile since it enables the use of many models to identify various types of clusters.
• It functions best with data that have intricate shapes or structures.

Drawbacks

• In particular for huge data, it is computationally more expensive than hierarchical clustering.
• It necessitates a deeper comprehension of statistical modelling methods because the model selection can have an impact on the outcome.
• The number of clusters must be predetermined, just like with K-means clustering.

Hierarchical clustering applications

Several fields, including but not restricted to biology, image processing, marketing, economics, and social network analysis, use hierarchical clustering on a daily basis.

1. Biology.

One of the largest difficulties in bioinformatics is the grouping of DNA sequences.

Hierarchical clustering is a tool that biologists can use to analyse the genetic links between species and divide them into taxonomic categories. Quick analysis and display of the underlying linkages are both facilitated by this.

2. Image processing.

In image processing, hierarchical clustering can be used to group comparable pixels or portions of an image based on their colour, brightness, or other characteristics. Other tasks including picture segmentation, image classification, and object identification may benefit from this.

3. Marketing.

For more effective marketing strategies and product recommendations, marketing professionals can utilise hierarchical clustering to create a hierarchy between various client categories based on their purchase behaviours. If a customer is a low, medium, or high spender, different products can be recommended to them in retail settings.

4. Examination of social networks.

When used effectively, social networks may be a fantastic source of useful information. Hierarchical clustering can be used to locate communities or groups, comprehend how they relate to one another, and determine the overall network structure.

Steps involved in the hierarchical clustering algorithm

The hierarchical clustering technique creates clusters by using distance measurements. The main steps in this generation process are as follows:

By eliminating missing data and performing any other actions that will make the data as clean as feasible, preprocess the data. This process is more typical for the majority of machine learning projects.

• Determine the distance between each pair of data points using a specific distance metric, such as the cosine similarity, Manhattan distance, or Euclidean distance. The Euclidean distance measure, however, is the default.
• Combine the closest two clusters by distance.
• Adjust the distance matrix to account for the newly discovered clusters.
• Continue doing steps 1, 2, and 3 until every cluster has been combined into one.

Conclusion 

Hierarchical Clustering is an important concept to learn in Python as it is indeed one of the big futures of Python.