Spectral clustering is an important and up-and-coming variant of some fairly standard clustering algorithms. It is a powerful tool to have in your modern statistics tool cabinet. Spectral clustering includes a processing step to help solve non-linear problems, such that they could be solved with ...
Abstract In recent years, spectral clustering has become one of the most popular modern clustering algorithms. It is simple to implement, can be solved e?ciently by standard linear algebra software, and very often outperforms traditional clustering algorithms such as the k-means algorithm. On the...
“traditional algorithms” such as k-means or single linkage, spectral clustering has many fundamental advantages. Results obtained by spectral clustering very often outperform the traditional approaches, spectral clustering is very simple to implement and can be solved efficiently by standard linear ...
SC method is based on the graph theory and is insensitive to the structure of data. Many traditional clustering problems have been solved by it. Recently, SC method has successfully been implemented in many fields such as information searching [15], bioinformatics [16], and image segmentation [...
example, direct clustering of cells according to their gene expression profiles may capture similarities according to the circadian rhythm phase and not their type, which can substantially hinder our ability to distinguish different cell types. Clustering the neurons yielded 14 distinct clusters, three ...
To illustrate the usefulness of the method, a numerical example of horizontally polarized shear waves is presented. The snapshots amply demonstrate how well the numerical absorbing condition works, a consequence of Chebyshev nodal clustering. The kernel operation of this time-stepping algorithm is ...
After a clustering is specified, the cost of that clustering can be measured. The general problem to be solved using graph theory is to assign class-labels to data points so as to maximize the cost function, i.e., minimize the “cut cost” C(S,K,y) of partitioning: C ( S ,...
In comparison, the accuracy of SMART-SDB shows a marked enhancement, with its RMSE being reduced to 1.3366 m compared with over 2 m obtained by LBR and PLBR; this improvement stems from its strategy of partitioning the feature space through K-NN clustering and allocating the optimal PLBR, ...
ponents, solved very efficiently using least angle regression 1 (LARS) [11]. Subsequently, d’Aspremont et al. [9] relaxed the hard cardinality constraint and solved for a convex ap- proximation using semi-definite programming. In [21, 22], Moghaddam et al. proposed a spectral boun...
According to the similarity matrix, Laplacian matrix L based on the Mahalanobis distance is obtained by calculation, the eigenvalues and eigenvectors are solved for matrix L, and the eigenvectors corresponding to the first k eigenvalues are taken to perform K-means clustering. The specific steps of...