In this module, you will learn about different clustering approaches. You will learn how to use clustering for customer segmentation, grouping same vehicles, and for weather stations. You will understand 3 main types of clustering including Partitioned-based Clustering, Hierarchical Clustering, and Density-based Clustering.
- To understand different types of clustering algorithms.
- To apply clustering on different types of data sets.
Question 1: Which statement is NOT TRUE about k-means clustering?
- A. [ ] The objective of k-means, is to form clusters in such a way that similar samples go into a cluster, and dissimilar samples fall into different clusters.
- B. [ ] k-means divides the data into non-overlapping clusters without any cluster-internal structure.
- C. [X] As k-means is an iterative algorithm, it guarantees that it will always converge to the global optimum.
Question 2: Which of the following are characteristics of DBSCAN? (Select all that apply)
- A. [ ] DBSCAN does not require one to specify the number of clusters such as k in k-means.
- B. [ ] DBSCAN can find arbitrarily shaped clusters.
- C. [ ] DBSCAN can find a cluster completely surrounded by a different cluster.
- D. [ ] DBSCAN has a notion of noise, and is robust to outliers.
- E. [X] All of the above.
Question 3: Which of the following is an application of clustering?
- A. [ ] Customer churn prediction
- B. [ ] Price estimation
- C. [X] Customer segmentation
- D. [ ] Sales prediction
Question 4: Which approach can be used to calculate dissimilarity of objects in clustering?
- A. [ ] Cosine similarity
- B. [ ] Euclidian distance
- C. [ ] Minkowski distance
- D. [X] All of the above
Question 5: How is a center point (centroid) picked for each cluster in k-means? (Select all that apply)
- A. [X] We can randomly choose some observations out of the data set and use these observations as the initial means.
- B. [X] We can create some random points as centroids of the clusters.
- C. [ ] We can select it through correlation analysis.