Clustering:
Clustering is a machine learning and data analysis technique that involves grouping similar data points or objects together based on their characteristics or features. The goal of clustering is to identify natural groupings or patterns in data, making it easier to understand and analyze complex datasets. It is often used for tasks such as customer segmentation, anomaly detection, image segmentation, and more. Clustering algorithms aim to maximize the similarity within clusters while minimizing the similarity between clusters, and they do not require labeled data for training. Popular clustering algorithms include K-Means, Hierarchical Clustering, and DBSCAN, among others.
K-Means Clustering: K-Means is a partitioning algorithm that aims to divide a dataset into K distinct, non-overlapping clusters. Here’s how it works:
- Initialization: Start by selecting K initial cluster centroids (representative points). These can be randomly chosen or based on some other method.
- Assignment: Assign each data point to the nearest cluster centroid, creating K clusters.
- Update Centroids: Recalculate the centroids of the K clusters based on the data points assigned to them.
- Repeat: Steps 2 and 3 are repeated until the clusters no longer change significantly, or a specified number of iterations is reached.
K-Means seeks to minimize the sum of squared distances between data points and their respective cluster centroids. It’s efficient and works well with large datasets, but it requires specifying the number of clusters (K) in advance.