Practice Types Of Clustering Algorithms (6.1.2) - Unsupervised Learning – Clustering & Dimensionality Reduction

Practice: Types of Clustering Algorithms

Lesson 6.1.2Unsupervised Learning – Clustering & Dimensionality Reduction

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9 questions

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6 sample questions

Question 1.
What does K in K-Means represent?
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It signifies the count of clusters.
Question 2.
What does a dendrogram represent?
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Think of it as a tree diagram.
3Question 3.
What is the main idea behind K-Means clustering?
- It creates one cluster for all points
- It partitions data into K clusters
- It requires no initial configuration
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Remember what K represents in K-Means.
4Question 4.
Is DBSCAN sensitive to outliers?
- True
- False
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Recall how DBSCAN treats points that don’t belong to any cluster.
Question 5.
Analyze a customer dataset using both K-Means and DBSCAN. Discuss how the results differ, particularly in terms of outlier detection and cluster shapes.
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Compare how each algorithm treats noise and cluster configuration.
Question 6.
Design a study that utilizes hierarchical clustering to analyze a dataset of your choice. Outline your steps and expected outcomes.
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Consider what data structure you want to reveal through clustering.

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