Automatic Detection of Cluster Structure Changes using Relative
... In order to analyze clustering changes, this paper uses the ReDSOM method (Denny et al. 2010) to compare two Self-Organizing Maps M(τ1 ) and M(τ2 ) trained from two snapshot datasets D(τ1 ) and D(τ2 ). In Denny et al. (2010), changes in cluster structure are identified through visualizations by anal ...
... In order to analyze clustering changes, this paper uses the ReDSOM method (Denny et al. 2010) to compare two Self-Organizing Maps M(τ1 ) and M(τ2 ) trained from two snapshot datasets D(τ1 ) and D(τ2 ). In Denny et al. (2010), changes in cluster structure are identified through visualizations by anal ...
k clusters
... Similar to k-means: cluster centers tend to lie in a low-dimensional manifold in the feature space Clustering is performed by having several units competing for the current object ...
... Similar to k-means: cluster centers tend to lie in a low-dimensional manifold in the feature space Clustering is performed by having several units competing for the current object ...
ICARUS, arxiv:0812:2373 - IDS-NF
... Clusters: a density of points considerably higher than outside the cluster DBSCAN* algorithm: the `density-neighbourhood’ ...
... Clusters: a density of points considerably higher than outside the cluster DBSCAN* algorithm: the `density-neighbourhood’ ...
Using Projections to Visually Cluster High
... automatically separate regions in a one- or twodimensional projection, we use a separator as introduced in definition 3. The density estimator used in the separator definition is defined in the one- or two-dimensional subspace of the particular projection. Besides simple partitioning hyperplanes, we ma ...
... automatically separate regions in a one- or twodimensional projection, we use a separator as introduced in definition 3. The density estimator used in the separator definition is defined in the one- or two-dimensional subspace of the particular projection. Besides simple partitioning hyperplanes, we ma ...
“Clustering - Classification” Model For Gene Expression Data
... Classification has been an important statistical data analysis tool in many fields. Particularly in computational biology and bioinformatics, clustering methods have been developed and applied extensively. In high throughput biological data sets such as those obtained from transcriptomics analysis, ...
... Classification has been an important statistical data analysis tool in many fields. Particularly in computational biology and bioinformatics, clustering methods have been developed and applied extensively. In high throughput biological data sets such as those obtained from transcriptomics analysis, ...
Multiple Non-Redundant Spectral Clustering Views
... use of the Hilbert-Schmidt Independence Criterion (HSIC) (Gretton et al., 2005) for this purpose. That is, we use the HSIC as a penalty that is added to our spectral clustering criterion. HSIC measures the statistical dependence among views and drives the learning algorithm toward finding views that ...
... use of the Hilbert-Schmidt Independence Criterion (HSIC) (Gretton et al., 2005) for this purpose. That is, we use the HSIC as a penalty that is added to our spectral clustering criterion. HSIC measures the statistical dependence among views and drives the learning algorithm toward finding views that ...
Research Study of Big Data Clustering Techniques
... or instances. Clustering groups data instances into subsets in such a manner that similar instances are grouped together, while different instances belong to different groups and the groups are called as clusters.Clustering algorithms have emerged as an alternative powerful meta-learning tool to acc ...
... or instances. Clustering groups data instances into subsets in such a manner that similar instances are grouped together, while different instances belong to different groups and the groups are called as clusters.Clustering algorithms have emerged as an alternative powerful meta-learning tool to acc ...
Nearest-neighbor chain algorithm
In the theory of cluster analysis, the nearest-neighbor chain algorithm is a method that can be used to perform several types of agglomerative hierarchical clustering, using an amount of memory that is linear in the number of points to be clustered and an amount of time linear in the number of distinct distances between pairs of points. The main idea of the algorithm is to find pairs of clusters to merge by following paths in the nearest neighbor graph of the clusters until the paths terminate in pairs of mutual nearest neighbors. The algorithm was developed and implemented in 1982 by J. P. Benzécri and J. Juan, based on earlier methods that constructed hierarchical clusterings using mutual nearest neighbor pairs without taking advantage of nearest neighbor chains.