Which of the following is true about K-Means clustering?Answer areaIt is a density-based clustering methodIt assigns each point to the nearest cluster centerIt creates a hierarchy of clustersIt can handle clusters of varying density

What is a disadvantage of K-Means clustering?Answer areaIt is easy to implementIt always converges to the global optimumIt is sensitive to the initial placement of centroidsIt works well with all shapes of clusters

Suppose you have a dataset of customer transactions from an online retail store. Each data point represents a customer and contains two features: "Total Amount Spent" (in pounds) and "Total Number of Items Purchased." You want to divide the customers into different groups based on their spending behaviour.Which of the following statements about K-means clustering applied to this dataset is true?Group of answer choicesThe number of clusters (K) is determined by the mean of "Total Amount Spent" and "Total Number of Items Purchased."K-means is sensitive to the initial placement of cluster centres, so it's essential to initialise them randomly.K-means will always produce the same clustering result, regardless of the initial positions of the cluster centres.K-means is not suitable for clustering real-valued data and can only handle categorical features.

What is an advantage of hierarchical clustering over K-Means?Answer areaIt is less computationally expensiveIt does not require specifying the number of clusters in advanceIt always finds the global optimumIt works better with large datasets

What is the main objective of the K-Means algorithm?Answer areaTo minimize the sum of squared distances between points and their respective cluster centroidsTo maximize the distance between different clustersTo minimize the number of clustersTo maximize the variance within each cluster

Which function in scikit-learn is used to perform K-Means clustering?Answer areaK Means Cluster ()K Means ()K Means Clustering ()Cluster K Means ()