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

What  is an application of K-Means clustering?Answer areaPredicting stock pricesCustomer segmentationSentiment analysisReal-time prediction

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

Question 1The objective of k-means clustering is:1 pointSeparate dissimilar samples and group similar onesMinimize the cost function via gradient descentYield the highest out of sample accuracyMaximize the number of correctly classified data points

In k-means clustering, k represents the

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.