In this module, you will learn about different clustering approaches. You learn how to use clustering for customer segmentation, grouping same vehicles, and also clustering of weather stations. You understand 3 main types of clustering, including Partitioned-based Clustering, Hierarchical Clustering, and Density-based Clustering.

Learning Objectives

• Explain the different types of clustering algorithms and their use cases.
• Describe the K-Means Clustering technique.
• Describe accuracy concerns for the K-Means Clustering technique.
• Explain the Hierarchical Clustering technique.
• Provide an overview of the agglomerative algorithm for hierarchical clustering.
• List the advantages and disadvantages of using Hierarchical Clustering.
• Describe the capabilities of the density-based clustering called DBSCAN.
• Apply clustering on different types of datasets.

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• k-Means Clustering

  • Lab 4-1: k-Means

• Hierarchical Clustering

  • Lab 4-2: Agglomerative Clustering/Hierarchical Clustering

• Density-based Clustering

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Quiz: Clustering

TOTAL POINTS 9

Question 1

Which of the following is an application of clustering?

• Customer churn prediction
• Price estimation
• Customer segmentation
• Sales prediction

Correct

Question 2

Which approach can be used to calculate dissimilarity of objects in clustering?

• Minkowski distance
• Euclidian distance
• Cosine similarity
• All of the above

Correct

Question 3

How is a center point (centroid) picked for each cluster in k-means?

• We can randomly choose some observations out of the data set and use these observations as the initial means.
• We can create some random points as centroids of the clusters.
• We can select it through correlation analysis.

Correct

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