Good to see you here!

Today’s quiz would be based on Cluster Analysis. So let’s get started.

**Question 1: What is Cluster Analysis?**

Cluster Analysis is the statistical procedure that is aimed at grouping data object based on the information found in the data set that describes the objects and their attributes

**Question 2: What is the Goal of Cluster Analysis?**

The objective of cluster analysis is to group objects with similar characteristics into one cluster.

**Question 3: What are the two types of Clustering?**

The two types of clustering are:

Hierarchical Clustering: Clusters are arranged in a hierarchical tree

Partitioning Clustering: Data are grouped into distinct subsets that does not overlap

**Question 4: Describe the k-Means Clustering**

K-Means clustering is a partitioning clustering approach where each cluster is associated with a centroid or center point and each data point is assigned to the centroid that is closest to it. The number of clusters is specified in advance.

**Question 5: Write the k-Means Clustering Algorithm?**

i. Choose the initial value of K

ii. **repeat**

iii. Form K clusters by assigning each point to the closest centroid

iv. Recalculate the centroid of each cluster

v. Move the centroid to the new computed position

vi. **until **The centroids position don’t change

**Question 6: How do you Choose Initial Value of K for k-Means Clustering**

- Use another clustering method to estimate it
- Run the algorithm with different values of K and then choose the one that is optimal
- Use the prior knowledge about the characteristics of the data

**Question 7: How do you choose the centroid for the cluster?**

- Random selection from the feature space
- Random selection from the data set
- Look for dense regions of space
- Space them uniformly around the feature space

**Question 8: How is the quality of a cluster measured?**

- The size of the cluster vs the distance between the clusters
- The Distance between members of the clusters
- The Diameter of the smallest sphere

**Question 9: What are some limitations of k-Means Clustering?**

Not efficient if data contains outliers

Fails for non-convex round clusters

**Question 9: What is McQueen’s Algorithm used for?**

The McQueen’s Algorithm is used for measuring the goodness of the clustering and for minimizing the compactness function in finite steps

**Question 10: Outline and explain the two types of Hierarchical Clustering**

The two types of hierarchical clustering are:

Top-Down Clustering

Bottom-Top Clustering

*How Bottom-Top or Agglomerative Clustering work*

- Start with each of the data points in its own cluster
- Merge two clusters that are similar
- Repeat the merging until there is a single cluster of all he data points

*How Top-Down or Divisive Clustering Work*

- Start with all examples in one big cluster
- Remove the data point that seems to far away from other points
- Repeat the process until all points is in its own cluster

**Question 11: Mention three ways to compute dissimilarity between clusters**

- Single Link
- Complete Link
- Group Average

**Question 12: Compare k-Means and Hierarchical Clustering**

k-Means produces single partition while hierarchical produces different partitions

k-Means needs the number of clusters specified in advance while hierarchical does not

k-Means is have a more efficient run-time than the hierarchical

**Question 13: What is a Dendrogram?**

A dendrogram is a tree diagram used to illustrate the arrangement of clusters in hierarchical clustering.

I would stop here so I can allow you some time to get your head around these concepts.

Thank you for reading.!

Feel free to check out the quiz on other Statistics topics.

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