What is the purpose of Cross-Validation for clustering tasks?Review LaterTo evaluate the performance of classification algorithms.To assess the quality and coherence of clustering results.To measure the accuracy of regression models.To validate the performance of anomaly detection algorithms.

The purpose of Cross-Validation for clustering tasks is to assess the quality and coherence of clustering results.

Here's a step-by-step explanation:

1. Clustering is an unsupervised learning method that groups similar instances into clusters.

2. The quality of a clustering algorithm is determined by how well it groups similar instances and separates dissimilar ones.

3. Cross-validation is a technique used to assess the predictive performance of a model. In the context of clustering, it can be used to evaluate how well the clustering model performs on unseen data.

4. During cross-validation, the data set is divided into 'k' subsets. The clustering algorithm is trained on 'k-1' subsets and the remaining subset is used for validation. This process is repeated 'k' times, with each subset used for validation once.

5. The results from each iteration are then averaged to give a final score. This score provides an estimate of how well the clustering model is likely to perform on unseen data.

6. Therefore, the purpose of cross-validation in clustering tasks is to assess the quality and coherence of clustering results. It provides a way to tune the model parameters to improve its performance.