How to do Affinity based Clustering in Python

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How to do Affinity based Clustering in Python

Affinity Propagation is a method of clustering that is based on the concept of “message passing” between data points. It works by creating a similarity matrix between all the data points and then iteratively updating the matrix to find the clusters. In this article, we will go over the basics of how to do Affinity Propagation Clustering in Python.

First, we need to import the necessary libraries such as Numpy and Pandas, which will help us handle our data. Next, we will import the AffinityPropagation class from the sklearn.cluster library, which will be used to create our clusters.

Once we have our libraries and classes imported, we can start creating our clusters. To do this, we will first need to load our data into a Numpy array.

Once our data is loaded, we can create our model by instantiating the AffinityPropagation class. We need to set the damping factor which is used to control the balance between exploration and exploitation during the iterations.

Once the model is instantiated, we can fit the model to our data using the fit_predict method. This will cluster our data into the specified number of clusters.

To check the accuracy of our model, we can use different metrics such as silhouette score and calinski harabasz index. The higher the silhouette score and calinski harabasz index, the better our model is at clustering the data.

In conclusion, doing Affinity Propagation Clustering in Python is a straightforward process. By using the sklearn.cluster library, we can easily load and cluster our data using the AffinityPropagation class. By tuning the damping factor and using metrics, we can optimise the accuracy of our model and make more accurate clusters. Affinity Propagation is a good method when the number of clusters is not known in advance, and the data has a lot of noise and overlaps between clusters.

 

In this Learn through Codes example, you will learn: How to do Affinity based Clustering in Python.



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