Identify and remove outliers in each clusters from K-Means clustering. This article will cover how to handle outliers after clustering data into several clusters using Silhouette Analysis.
The real-world data often has a lot of outlier values. The cause of outliers can be data corruption or failure to record data. The handling of outliers is very important during the data preprocessing pipeline as the presence of outliers can prevent the model to perform best.
There are various strategies to handle outliers in the dataset. This article will cover how to handle outliers after clustering data into several clusters using Silhouette Analysis.
The** silhouette method is a method to find the optimal number of clusters and interpretation and validation of consistency within clusters of data. The silhouette method computes silhouette coefficients of each point that measure how much a point is similar to its own cluster compared to other clusters. by providing a **succinct graphical representation of how well each object has been classified. The analysis of these graphical representations is called Silhouette Analysis.
The silhouette value is a measure of how similar an object is to its own cluster (cohesion) compared to other clusters (separation). The value of the silhouette ranges between [1, -1].
Important Points: The Silhouette coefficient of +1 indicates that the sample is far away from the neighboring clusters. The Silhouette coefficient of 0 indicates that the sample is on or very close to the decision boundary between two neighboring clusters. Silhouette coefficient <0 indicates that those samples might have been assigned to the wrong cluster or are outliers.
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