K-means clustering in beginner-friendly terms:
What is K-means clustering?
K-means clustering is an unsupervised machine learning algorithm used to automatically group or cluster similar data points together.
How does it work?
K-means clustering works by defining ‘K’ number of clusters or groups ahead of time. The algorithm then assigns each data point to one of these K clusters based on feature similarity. The features could be things like age, income, spending habits etc.
It then calculates the ‘center’ of each cluster. This is called the centroid. Next it recalculates cluster membership by finding which cluster center each point is closest to. This process repeats until the membership assignments no longer change.
Why use K-means clustering?
The main reasons to use K-means clustering are:
Grouping Data: It automatically organizes unlabeled data points into meaningful clusters or groups.
Pattern Recognition: Clustering helps recognize hidden patterns in unstructured data and gain insights.
Data Segmentation: Identifying distinct groups in data allows treating each segment differently for tasks like targeting customers.
Data Compression: Cluster IDs can replace raw data for storing, visualizing or processing large datasets.
How is it applied?
K-means clustering is commonly used for customer segmentation, image recognition, compiler optimization, gene expression analysis and more. It works best with numerical data and when you have a general idea of ‘K’ clusters to aim for.
In summary, K-means clustering provides an automatic way to group messy data into organized, interpretable clusters based on similarities between data points.
