6.2 Recommendations Worth a Million: An Introduction to Clustering | 6.2 Recommendations Worth a Million: An Introduction to Clustering | 6 Clustering | The Analytics Edge | Sloan School of Management

<Video 5: Hierarchical Clustering
6.2.1Video 1: Introduction to Netflix
6.2.2Quick Question
6.2.3Video 2: Recommendation Systems
6.2.4Quick Question
6.2.5Video 3: Movie Data and Clustering
6.2.6Quick Question
6.2.7Video 4: Computing Distances
6.2.8Quick Question
6.2.9Video 5: Hierarchical Clustering
6.2.10Quick Question
6.2.11Video 6: Getting the Data
6.2.12Quick Question
6.2.13Video 7: Hierarchical Clustering in R
6.2.14Quick Question
6.2.15Video 8: The Analytics Edge of Recommendation Systems
>Video 6: Getting the Data

Quick Question

Suppose you are running the Hierarchical clustering algorithm with 212 observations.

How many clusters will there be at the start of the algorithm?

How many clusters will there be at the end of the algorithm?

Explanation

The Hierarchical clustering algorithm always starts with each data point in its own cluster, and ends with all data points in the same cluster. So there will be 212 clusters at the beginning of the algorithm, and 1 cluster at the end of the algorithm.