What is Spectral Clustering?
Spectral clustering is an advanced technique in unsupervised learning used in machine learning. It leverages the spectral properties of the similarity matrix to identify clusters within data points. The primary idea is to represent the data in a lower-dimensional space where the clusters become more distinct and easier to identify.
The process begins by creating a similarity graph of the data, where nodes represent data points, and edges denote the similarity between them. Common measures of similarity include Euclidean distance or cosine similarity. The next step involves constructing the Laplacian matrix from the similarity graph, which captures the structure of the data.
From the Laplacian matrix, eigenvalues and eigenvectors are computed. The eigenvectors corresponding to the smallest eigenvalues are used to form a new representation of the data in a lower-dimensional eigenspace. Clustering methods such as K-means can then be applied to this transformed representation to identify distinct clusters.
Spectral clustering is particularly useful for complex dataset shapes that traditional clustering methods (like K-means) may not effectively separate. It is widely used in various applications, including image segmentation, social network analysis, and any domain where understanding the relationships among data points is crucial.
Overall, spectral clustering is a powerful tool in the arsenal of data scientists and software developers, providing them with an effective means to uncover hidden patterns in data.