6.3 Predictive Diagnosis: Discovering Patterns for Disease Detection | 6.3 Predictive Diagnosis: Discovering Patterns for Disease Detection | 6 Clustering | The Analytics Edge | Sloan School of Management

<Video 3: Predicting Heart Attacks using Clustering
6.3.1Video 1: Heart Attacks
6.3.2Quick Question
6.3.3Video 2: The Data
6.3.4Quick Question
6.3.5Video 3: Predicting Heart Attacks using Clustering
6.3.6Quick Question
6.3.7Video 4: Understanding Cluster Patterns
6.3.8Quick Question
6.3.9Video 5: The Analytics Edge
>Video 4: Understanding Cluster Patterns

Quick Question

K-means clustering differs from Hierarchical clustering in a couple important ways. Which of the following statements is true?

In k-means clustering, you have to pick the number of clusters you want before you run the algorithm.

In k-means clustering, you can pick the number of clusters you want after the algorithm is done, just like in Hierarchical clustering.

Explanation

In k-means clustering, you have to pick the number of clusters before you run the algorithm, but the computational effort needed is much less than that for hierarchical clustering (we'll see this in more detail during the recitation).