MACHINE LEARNING

Detailed explanation-1: -Out of all the options, the K-Means clustering algorithm is most sensitive to outliers as it uses the mean of cluster data points to find the cluster center.

Detailed explanation-2: -One disadvantage of the K-means algorithm is that it is sensitive to the initialization of the centroids or the mean points.

Detailed explanation-3: -In K-Means clustering outliers are found by distance based approach and cluster based approach. In case of hierarchical clustering, by using dendrogram outliers are found. The goal of the project is to detect the outlier and remove the outliers to make the clustering more reliable. clustering more reliable.