What is Clustering in Unsupervised Learning?
Clustering is a fundamental technique in the field of unsupervised learning, which itself is a subcategory of machine learning, falling under the broader umbrella of artificial intelligence (AI). Unlike supervised learning, where models are trained on labeled data, clustering involves grouping a set of objects based solely on their features and attributes, without any predefined labels.
The primary goal of clustering is to arrange the data into clusters, where items within the same group are more similar to each other than to those in other groups. This similarity can be based on various metrics such as distance measures (Euclidean, Manhattan, etc.) or more complex statistical techniques.
Common algorithms employed for clustering include:
- K-means Clustering: Partitions data into K distinct clusters by minimizing variance within each cluster.
- Hierarchical Clustering: Builds a hierarchy of clusters either through agglomerative (bottom-up) or divisive (top-down) approaches.
- DBSCAN: Identifies clusters based on the density of data points, allowing it to handle noise more effectively.
Clustering has diverse applications across various domains, such as market segmentation, social network analysis, and image segmentation. In essence, it serves as a powerful tool to uncover hidden patterns and relationships in large datasets, thereby aiding organizations in making data-driven decisions.