An Interactive View for Hierarchical Clustering
نویسنده
چکیده
This paper describes a visualization of a general hierarchical clustering algorithm that allows the user to manipulate the number of classes produced by the clustering method without requiring a radical redrawing of the clustering tree. The visual method used, a space-filling recursive division of a rectangular area, keeps the items under consideration at the same screen position even while the number of classes is under interactive control. As well as presenting a compact representation of the clustering with different cluster numbers, this method is particularly useful in a linked views environment where additional information can be added to a display to encode other information, without this added level of detail being perturbed when changes are made to the number of clusters.
منابع مشابه
An Easy Viewer for Out-of-core Visualization
In this paper, we propose a viewer for huge point-sampled models by combining out-of-core technologies with view-dependent level-of-detail (LOD) control. This viewer is designed on the basis of a multiresolution data structure we have developed for gaze-guided visualization and transmission of 3D point sets. In order to reduce memory loads for huge point sets on general PC platforms, we introdu...
متن کاملCombining Automated and Interactive Visual Analysis of Biomechanical Motion Data
We present a framework for combining automated and interactive visual analysis techniques for use on high-resolution biomechanical data. Analyzing the complex 3D motion of, e.g., pigs chewing or bats flying, can be enhanced by providing investigators with a multi-view interface that allows interaction across multiple modalities and representations. In this paper, we employ nonlinear dimensional...
متن کاملUnderstanding Hierarchical Clustering Results by Interactive Exploration of Dendrograms: A Case Study with Genomic Microarray Data
Hierarchical clustering is widely used to find patterns in multi-dimensional datasets, especially for genomic microarray data. Finding groups of genes with similar expression patterns can lead to better understanding of the functions of genes. Early software tools produced only printed results, while newer ones enabled some online exploration. We describe four general techniques that could be u...
متن کاملClustering Large Datasets and Visualizations of Large Hierarchies and Pyramids Symbolic Data Analysis Approach
In the paper we present an approach to clustering large datasets of mixed units described by symbolic objects in form of histograms of values of variables. For visualization of (large) hierarchies and pyramids we present two solutions: hyperbolic display and flags. The hyperbolic display is an example of fish-eye displays that allow a closer look at the data in the selected neighborhood put int...
متن کاملProtoNet: hierarchical classification of the protein space
The ProtoNet site provides an automatic hierarchical clustering of the SWISS-PROT protein database. The clustering is based on an all-against-all BLAST similarity search. The similarities' E-score is used to perform a continuous bottom-up clustering process by applying alternative rules for merging clusters. The outcome of this clustering process is a classification of the input proteins into a...
متن کامل