Clustering is a fundamental technique in unsupervised machine learning and data analysis. It involves grouping similar data points together based on certain features or characteristics. Clustering is used for various purposes, such as discovering patterns in data, segmenting customers, image analysis.
Key Concepts in Clustering:
Unsupervised Learning: Clustering is an unsupervised learning technique, which means it doesn’t rely on labeled data. Instead, it identifies patterns and structures within data based on inherent similarities.The main objective of clustering is to find groups or clusters of data points where points within the same cluster are more similar to each other than to those in other clusters. Clusters are defined based on some similarity or distance metric.
Distance Metrics: Clustering algorithms typically use distance metrics to measure the similarity or dissimilarity between data points. Common distance metrics include Euclidean distance, Manhattan distance, cosine similarity, and more.
Centroids: Many clustering algorithms are centroid-based. They identify cluster centers (centroids) and assign data points to the nearest centroid. The choice of centroids and the distance metric can vary depending on the algorithm.
Hierarchical vs. Partitional Clustering: Clustering methods can be broadly categorized into hierarchical and partitional clustering
- Hierarchical Clustering: It creates a tree-like structure of clusters, where clusters at one level are merged to form larger clusters at the next level. It can result in a hierarchical structure of clusters.
- Partitional Clustering: It divides data into non-overlapping clusters, with each data point belonging to exactly one cluster. Common partitional methods include K-Means and DBSCAN.
Some Clustering Algorithums:
K-Means: This is one of the most popular partitional clustering algorithms. It aims to partition data into K clusters, where K is a user-defined parameter. It uses centroids to represent each cluster.
Hierarchical Clustering: Algorithms like Agglomerative and Divisive clustering create a hierarchy of clusters.
DBSCAN (Density-Based Spatial Clustering of Applications with Noise): DBSCAN groups together data points that are close to each other and separates areas with low data density.