How to do DBSCAN based Clustering in Python
DBSCAN (Density-Based Spatial Clustering of Applications with Noise) is a method of clustering that is based on the density of the data points. It works by identifying clusters of high density and then expanding them to include nearby points that are also of high density. In this article, we will go over the basics of how to do DBSCAN 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 DBSCAN 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 DBSCAN class. We need to set the eps (maximum distance between two samples for them to be considered as in the same neighborhood) and the minimum number of samples in a neighborhood to form a dense region.
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. The higher the silhouette score, the better our model is at clustering the data.
In conclusion, doing DBSCAN Clustering in Python is a straightforward process. By using the sklearn.cluster library, we can easily load and cluster our data using the DBSCAN class. By tuning the eps and the minimum number of samples in a neighborhood, and using metrics, we can optimise the accuracy of our model and make more accurate clusters. DBSCAN algorithm is a density-based clustering method, unlike k-means or hierarchical clustering, it can find clusters of arbitrary shape and clusters can have a varying number of observations.
In this Learn through Codes example, you will learn: How to do DBSCAN based Clustering in Python.
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