Which of the following is TRUE about clustering?Clustering methods find the similarities between objects according to the object attributes and group the similar objects into clusters.By adding more variables about the customers, the task of finding meaningful groupings in clustering becomes more complex.Clustering analysis can help answer questions about natural groupings of the dataset.All of the Above

Which of the following is an advantage of clustering?Answer areaHigh interpretability of resultsRequires labeled data for trainingCan discover hidden patterns in data without supervisionSuitable for all types of data

What is the main difference between classification and clustering? Explain using concrete examples.

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.

Which of the following statements regarding clustering are true?<br /> A. 1. DBSCAN can identify noise points in addition to clusters. <br />B. 2. DBSCAN requires the user to specify the number of clusters beforehand. <br />C. 3. Hierarchical clustering results depend on distance and linkage criteria. <br />D. 4. K-means clustering guarantees convergence to the global optimum.

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