clustering-study-group
- Bioinformatics Algorithms: An Active Learning Approach
- DBSCAN: Density-based spatial clustering of applications with noise (DBSCAN) is a data clustering algorithm proposed by Martin Ester, Hans-Peter Kriegel, Jörg Sander and Xiaowei Xu in 1996.[1] It is a density-based clustering algorithm: given a set of points in some space, it groups together points that are closely packed together (points with many nearby neighbors), marking as outliers points that lie alone in low-density regions (whose nearest neighbors are too far away). DBSCAN is one of the most common clustering algorithms and also most cited in scientific literature.[2]
-Dimensionality Reduction book chapter from Massive Mining Data Sets In this chapter we shall explore the idea of dimensionality reduction in more detail. We begin with a discussion of eigenvalues and their use in “principal component analysis” (PCA). We cover singular-value decomposition, a more powerful version of UV-decomposition. Finally, because we are always interested in the largest data sizes we can handle, we look at another form of decomposition, called CUR-decomposition, which is a variant of singularvalue decomposition that keeps the matrices of the decomposition sparse if the original matrix is sparse. -Massive Mining Data Sets book -CS246: Mining Massive Data Sets
- spectral-clustering-for-beginners: 1. Create a similarity graph between our N objects to cluster. 2. Compute the first k eigenvectors of its Laplacian matrix to define a feature vector for each object. 3. Run k-means on these features to separate objects into k classes.
- A Tutorial on Spectral Clustering by Ulrike von Luxburg
- Spectral Clustering Tutorial by Marina Meila
- Learning Eigenfunctions Links