40.Which of the following is correct regarding Divisive clustering?  A. initial stage is single cluster with all samples and process proceed by splitting intermediate cluster until all elements are separated  B. process starts from bottom and proceeds by merging cluster until stop criteria reached  C. requires prior knowledge of no of clusters you want to divide data into  D. Bottom-up approach

What is the final resultant cluster size in Divisive algorithm, which is one of the hierarchical clustering approaches?Review LaterZeroThreeTwosingleton

Which of the following is an effective way to begin a clustering exercise?

38.Hierarchical clustering would come under which of the following?  A. Supervised  B. Unsupervised  C. Reinforcement  D. Depends on the problem

Example 10.3 Agglomerative versus divisive hierarchical clustering. Figure 10.6 shows the appli- cation of AGNES (AGglomerative NESting), an agglomerative hierarchical clustering method, and DIANA (DIvisive ANAlysis), a divisive hierarchical clustering method, on a data set of five objects, {a, b, c, d, e}. Initially, AGNES, the agglomerative method, places each object into a cluster of its own. The clusters are then merged step-by-step according to some criterion. For example, clusters C1 and C2 may be merged if an object in C1 and an object in C2 form the minimum Euclidean distance between any two objects from solve

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.