kmedoids code

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Questions and Answers

What is the primary objective of the K-medoid clustering algorithm?

To find the longest distance between two data points

To minimize the sum of distances between data points and their cluster medoids (correct)

To maximize the sum of distances between data points and their cluster medoids

To calculate the average distance between all data points

How are the initial medoids selected in the K-medoid clustering algorithm?

Randomly selected from the entire dataset (correct)

Calculated using a weighted average of all data points

Selected based on the median value of each feature

Manually chosen by the user

What is the distance between point P1(8,4) and point (9,6)?

3 (correct)

What is the purpose of Step 3 in the K-medoid clustering algorithm?

To update the medoid of each cluster if a data point results in a lower cost Signup and view all the answers

Which points are assigned to medoid P2(4,6)?

(4,4), (5,8), (3,8), (2,5) Signup and view all the answers

What is the output of the K-medoid clustering algorithm?

A set of K clusters with their corresponding medoids Signup and view all the answers

What is the total cost involved in the assignment?

19 Signup and view all the answers

What is the distance metric used in the numerical example provided?

Custom distance metric: |𝑋2 − 𝑋1| + |𝑌2 − 𝑌1| Signup and view all the answers

What are the coordinates of the first medoid P1?

(8,4) Signup and view all the answers

What is the value of K in the numerical example?

2 Signup and view all the answers

What is the distance between point P2(4,6) and point (3,8)?

3 Signup and view all the answers

What are the coordinates of the second medoid P2?

(4,6) Signup and view all the answers

What is the total cost involved in swapping the medoids?

1 Signup and view all the answers

What is the value of C1 in the given data?

19 Signup and view all the answers

What is the primary goal of clustering in unsupervised machine learning?

To partition a set of data points into groups based on similarity Signup and view all the answers

What is the main difference between K-means and K-medoids algorithms?

K-means uses centroids, while K-medoids uses medoids Signup and view all the answers

What is the purpose of calculating the total cost involved in swapping the medoids?

To decide whether to revert the swap or not Signup and view all the answers

What is the final clustering outcome based on the given data?

Two clusters with P1(8,4) and P2(4,6) as medoids Signup and view all the answers

What is a key characteristic of a good clustering method?

High intra-class similarity and low inter-class similarity Signup and view all the answers

What is the clustering algorithm used in the given data?

K-Medoids Signup and view all the answers

What is the purpose of clustering in market segmentation?

To group similar customers together based on their characteristics Signup and view all the answers

What is the value of k in the given clustering problem?

2 Signup and view all the answers

What is the K in K-medoids clustering?

A user-defined parameter that determines the number of clusters Signup and view all the answers

What is the result of clustering high-dimensional data?

The data becomes lower-dimensional and easier to analyze Signup and view all the answers

Study Notes

Clustering

Clustering is a type of unsupervised machine learning that involves grouping similar data points together into clusters.
The goal of clustering is to partition a set of data points into groups (also called clusters) such that data points in the same group are more similar to each other than to those in other groups.
Clustering is used in a wide range of applications, including market segmentation, image segmentation, document classification, and anomaly detection.
The process of clustering can help to reveal patterns and relationships within the data, and can also be used to reduce the dimensionality of high-dimensional data.
There are various algorithms for clustering, including K-means, K-medoid, hierarchical clustering, and density-based clustering.
The choice of algorithm depends on the characteristics of the data and the requirements of the application.

K-Medoid Clustering

K-medoid is a clustering algorithm that groups similar data points together into clusters.
It is a variation of the K-means algorithm and is used to partition a set of data points into K clusters, where K is a user-defined parameter.
The K-medoid algorithm works by selecting K data points as the initial medoids and then assigning each data point to the closest medoid.
The algorithm then iteratively updates the medoids to minimize the sum of distances between data points and their cluster medoids.
The quality of a clustering result depends on both the similarity measure used by the method and its implementation.

K-Medoid Clustering Algorithm

Step 1: Initialization - Select K data points randomly as the initial medoids.
Step 2: Assignment - For each data point, calculate the distance to all K medoids and assign the data point to the closest medoid.
Step 3: Update - For each cluster, calculate the cost of replacing its medoid with each of its data points. If replacing the medoid with a data point results in a lower cost, update the medoid to that data point.
Step 4: Repeat step 2 and 3 until the medoid no longer changes or the maximum number of iterations is reached.
Step 5: Output - The final set of K medoids and the data points assigned to each cluster.

K-Medoid Clustering Numerical Example

Let us consider a set of data with k=2 and the distance formula used is Dist =|𝑋2 − 𝑋1| + |𝑌2 − 𝑌1|.
The data points are assigned to two clusters based on the distance calculation.
The total cost involved in the initial assignment is calculated.
The algorithm then iteratively updates the medoids to minimize the total cost.
The final medoids and clusters are obtained.

K-Medoid Clustering in Python

The data can be read from a list or a CSV file.
The K-medoid clustering algorithm can be implemented using Python.

Questions

Write a Python code to divide the data into clusters with k=2 using the K-Medoids clustering algorithm.
Check the answer theoretically.

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