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What is Clustering method in unsupervised learning?
An method known as unsupervised learning discovers patterns in untagged data. The idea is to compel the computer to create a clear representation of its surroundings and then produce innovative material from it through imitation, which is a crucial learning method for people.
There are several ways to evaluate the performance of a clustering method in unsupervised learning. Some common evaluation metrics include:
- The sum of distances between objects in the same cluster (also known as the intra-cluster distance): This measure indicates how closely the objects in each cluster are related to each other. A good clustering method will have a low intra-cluster distance, indicating that the objects within each cluster are similar to each other.
- The distances between different clusters (also known as the inter-cluster distance): This measure indicates how distinct the different clusters are from each other. A good clustering method will have a high inter-cluster distance, indicating that the clusters are well separated from each other.
- The predicted labels are equal to the actual labels: This measure is only applicable if you have labeled data and you are using the clustering method to classify the data into different categories. If the predicted labels are equal to the actual labels, it means that the clustering method is correctly identifying the different categories.
- The SVM accuracy on the data: This measure is only applicable if you are using a support vector machine (SVM) as a classifier after the clustering step. The SVM accuracy indicates how well the SVM is able to classify the data based on the clusters produced by the clustering method. A good clustering method will result in high SVM accuracy.
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