K Means Clustering

Description

This is a clustering algorithm that starts out with k classes and divides a set into k clusters.

Applications

K-means clustering algorithm has many applications, including:

• Anomaly detection
• Customer segmentation
• Picture segmentation
• Grouping similar data items based on their differences and similarities

Advantages/Disadvantages

Advantages of K-means clustering algorithm include:

• Relatively simple to implement
• Scales to large data sets
• Guarantees convergence
• Can warm-start the positions of centroids
• Easily adapts to new examples
• Generalizes to clusters of different shapes and sizes, such as elliptical clusters

Disadvantages of K-means clustering algorithm include:

• It is a bit difficult to predict the number of clusters i.e. the value of k
• Output is strongly impacted by initial inputs like number of clusters (value of k)
• Order of data will have a strong impact on the final output
• It is very sensitive to rescaling
• It is not good in doing clustering job if the clusters have a complicated geometric shape

Related

1. K-means Advantages and Disadvantages | Machine Learning - Google for Developers
2. Clustering Algorithms - K-means Algorithm - Tutorialspoint
3. K-Means Pros & Cons | HolyPython.com
4. Pros and Cons of K-means Clustering - LinkedIn
5. The Drawbacks of K-Means Algorithm | Baeldung on Computer Science
6. When to use K-means clustering - Crunching the Data
7. [[hierarchical-clustering]]
8. [[machine-learning]]

Citations: [1] https://developers.google.com/machine-learning/clustering/algorithm/advantages-disadvantages [2] https://www.tutorialspoint.com/machine_learning_with_python/clustering_algorithms_k_means_algorithm.htm [3] https://holypython.com/k-means/k-means-pros-cons/ [4] https://www.linkedin.com/pulse/pros-cons-k-means-clustering-aashima-yuthika [5] https://www.baeldung.com/cs/k-means-flaws-improvements [6] https://crunchingthedata.com/when-to-use-k-means-clustering/