Identify the clusters in Figure 7.36 using the center-, contiguity-, and density-based definitions. Also indicate the number of clusters for each case and give a brief indication of your reasoning. Note that darkness or the number of dots indicates density. If it helps, assume center-based means Kmeans, contiguity-based means single link, and density-based means DBSCAN

2) Given a Clustering task, how you can evaluate the performance on the test set and how wewould know if the clusters are correct. Explain any three possible solutions.

We propose adding a tier to Flynn's taxonomy. Do these devices have any distinguishing features?

After learning about the k-means clustering algorithm in the big data course, some of your classmates tell you that they are not very enthusiastic about using it. The main reason they provide is that, when applied to the same dataset, the algorithm seems to be giving different clusters every times it is run. What should you say to them?     You should explain to them that they are interpreting the computer output incorrectly. Even though K-means seems to give different clusters every time it is run on the same dataset, if they look more closely at those clusters, they will notice that they are really the same clusters, but with different labels.     You should explain to them that they are using the computer functions incorrectly. The K-means algorithm always results in the same clusters.     You should explain to them that they should run the k-means algorithm several times and then pick up the clusters with the smallest objective function (all while warning them…