K-Means Clustering

Question 1

Is K-Means Clustering supervised or unsupervised?

Answer 1

K-Means Clustering is an example of unsupervised machine learning.

Question 2

Explain supervised versus unsupervised learning

Answer 2

In unsupervised learning, there is no specific output. The data is analyzed without knowing a specific output you’re looking for.

Question 3

Name some examples of clustering

Answer 3

Market segmentation, product analysis, etc.

Question 4

What is a cluster

Answer 4

A cluster is a collection of objects that are similar

Question 5

How do we determine similarity in clustering?

Answer 5

We need a notion of distance

Question 6

What is the objective of clustering?

Answer 6

The objective of clustering is to group similar data points into a group. Some examples are segmenting customers into similar groups, or automatically organizing files emails into folders.

Question 7

How does clustering simplify data?

Answer 7

Clustering simplifies data by reducing many data points into a few clusters

Question 8

What are some examples of distance used in clustering?

Answer 8

Examples of common distance measures in clustering are Manhattan Distance, Euclidian Distance, and Chebyshev Distance.

Question 9

What is the formula for Euclidian distance?

Answer 9

Square root of [ X1 - X2 squared + Y1 - Y2 squared + Z1 - Z2 squared + m…]

m number of columns

Question 10

What is the formula for Manhattan distance?

Answer 10

Absolute value of X1 minus X2 plus absolute value of Y1 minus Y2 plus absolute value of Z1 minus Z2, etc.

Question 11

Why is it called Manhattan distance?

Answer 11

In Manhattan, you cannot connect two points directly. You must walk in a grid.

Question 12

How do you calculate Chebyshev or chessboard distance?

Answer 12

Take the max value of (absolute value X1 - X2 or absolute value of Y1 - Y2 or absolute value of Z1 - Z2 … etc)

Question 13

What is the Minkowski distance?

Answer 13

A formula which uses a P value, depending on which distance measure you want. It is calculated by the sum of (all absolute value of Xi - Yi raised to the power of P) then raised to the power of 1/p.

Euclidian distance uses P equals two, Manhattan uses P equals one, chessboard equals P is greater than two and up to infinity.

Question 14

What measure of distance does K means clustering use?

Answer 14

Euclidian distance

Question 15

Name some types of clustering

Answer 15

connectivity based clustering
Centroid based clustering

Question 16

What is connectivity based clustering?

Answer 16

Based on the idea that related objects are closer to each other

Question 17

Formula for determining number of connections between N points

Answer 17

(N x (N-1)) / 2

Question 18

What is the point of attempting to choose the optimal K and what is a commonly used method for doing so?

Answer 18

You are attempting to strike a good balance between compression and accuracy. The elbow method is commonly used.

Question 19

Using K means, how do you calculate the centroid of a cluster

Answer 19

The centroid of a cluster is calculated by finding the mean vector of all data points in that cluster. For example, add up the absolute values of X and divide by the number of data points to find the X value, and add up all of the absolute value of Y data points and divide by the number of data points to find the Y value.