Which of the following is NOT a common application of the k-means algorithm?Select one:a.Image compressionb.Anomaly detectionc.Customer segmentationd.Regression analysis

Which of the following is a common application of K-Means clustering?Answer areaImage compressionPredicting stock pricesSpam filteringSentiment analysis

What is the main goal of the k-means algorithm?Select one:a.To discover patterns or relationships within a datasetb.To partition a dataset into a specified number of clustersc.To classify data into predefined categoriesd.To predict the value of a continuous target variable

Which of the following is NOT a limitation of the k-means algorithm?Select one:a.It is not affected by the scale of the variablesb.It may produce suboptimal results if the clusters are not sphericalc.It is sensitive to the initial placement of centroidsd.It requires the user to specify the number of clusters in advance

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

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.