Chapter 9. Data Visualization and High-Dimensional Data Clusteringdata visualization and high‐dimensional data clusteringisometric feature mapping (ISOMAP) algorithmprojected and subspace clusteringThis chapter contains sections titleddoi:10.1002/9780470382776.ch9Xu, Rui...
data clusteringdata miningmultidimensional scalingexploratory data analysislearning vector quantizationA common task in data mining is the visualization of multivariate objects on scatterplots, allowing human observers to perceive subtle inter-relations in the dataset such as outliers, groupings or other ...
The exploratory investigation of multivariate time series (MTS) may become extremely difficult, if not impossible, for high dimensional datasets. Paradoxically, to date, little research has been conducted on the exploration of MTS trough unsupervised clustering and visualization. In this chapter, the au...
Clustering in high-dimensional spaces is nowadays a recurrent problem in many scientific domains but remains a difficult task from both the clustering accuracy and the result understanding points of view. This paper presents a discriminative latent mixture (DLM) model which fits the data in a latent...
The recent development of new and often very accessible frameworks and powerful hardware has enabled the implementation of computational methods to generate and collect large high dimensional data sets and created an ever increasing need to explore as well as understand these data [1,2,3,4,5,6,...
Kohonen maps are often used for visualizing high-dimensional feature vectors in lowdimensional space. This approach is often recommended for supporting the clustering of data. In this paper an alternative approach is proposed which is more in the lines of multivariate statistics and provides a simulta...
Scientifically, the algorithm uses affinity propagation for clustering and two-dimensional graphs for visualizing online game data. The algorithm analyzes the Overwatch game data for the discovery of new knowledge about current players and the clustering of data for each hero character. This knowledge ...
The self-organizing map artificial neural network takes high-dimensional data and produces a diagram (map) that displays it in one or two dimensions. In short, humans can visualize interactions when displayed in one, two, or three dimensions, but not four or more dimensions. Data composed of ...
Best used for Visualizing complex, high-dimensional data Data with linear structure Output Low-dimensional representation Principal components Use cases Clustering, anomaly detection, NLP Noise reduction, feature extraction Computational intensity High Low Interpretation Harder to interpret Easier to interpret ...
VISDA performs progressive, divisive hierarchical clustering and visualization, supported by hierarchical mixture modeling, supervised/unsupervised informative gene selection, supervised/unsupervised data projection, and user/prior knowledge guidance, to discover hidden clusters within complex, high-dimensional ...