Results 1 - 10 of 56

Clustering of the Self-Organizing Map

by Juha Vesanto, Esa Alhoniemi , 2000
"... The self-organizing map (SOM) is an excellent tool in exploratory phase of data mining. It projects input space on prototypes of a low-dimensional regular grid that can be effectively utilized to visualize and explore properties of the data. When the number of SOM units is large, to facilitate quant ..."
Abstract - Cited by 280 (1 self) - Add to MetaCart
quantitative analysis of the map and the data, similar units need to be grouped, i.e., clustered. In this paper, different approaches to clustering of the SOM are considered. In particular, the use of hierarchical agglomerative clustering and partitive clustering using-means are investigated. The two

Radial Clustergrams: Visualizing the Aggregate Properties of Hierarchical Clusters

by Dimitris K. Agrafiotis, Deepak Bandyopadhyay, Michael Farnum , 2007
"... A new radial space-filling method for visualizing cluster hierarchies is presented. The method, referred to as a radial clustergram, arranges the clusters into a series of layers, each representing a different level of the tree. It uses adjacency of nodes instead of links to represent parent-child r ..."
Abstract - Cited by 3 (1 self) - Add to MetaCart
-child relationships and allocates sufficient screen real estate to each node to allow effective visualization of cluster properties through color-coding. Radial clustergrams combine the most appealing features of other cluster visualization techniques but avoid their pitfalls. Compared to classical dendrograms

Hierarchical Aligned Cluster Analysis for Temporal Clustering of Human Motion

by Feng Zhou, Fernando De La Torre, Jessica K. Hodgins , 2013
"... Temporal segmentation of human motion into plausible motion primitives is central to understanding and building computational models of human motion. Several issues contribute to the challenge of discovering motion primitives: the exponential nature of all possible movement combinations, the variab ..."
Abstract - Cited by 31 (2 self) - Add to MetaCart
, the variability in the temporal scale of human actions, and the complexity of representing articulated motion. We pose the problem of learning motion primitives as a temporal clustering one, and derive an unsupervised hierarchical bottom-up framework called hierarchical aligned cluster analysis (HACA). HACA finds

Hierarchical Clustering Algorithm- A Comparative Study Dr.N.Rajalingam

by unknown authors
"... Clustering is a data mining (machine learning) technique used to place data elements into related groups without advance knowledge on the group definitions. In this paper the authors provides an in depth explanation of implementation of agglomerative and divisive clustering algorithms for various ty ..."
Abstract - Add to MetaCart
types of attributes. Database- the details of the victims of Tsunami in Thailand during the year 2004, was taken as the test data. The algorithms are implemented using Visual programming and the formation of the clusters and running time needed of the algorithms using different linkages (agglomerative

Comparing Chemistry to Outcome: The Development of a Chemical Distance Metric, Coupled with Clustering and Hierarchal Visualization Applied to Macromolecular

by Andrew E. Bruno, A M. Ruby, Joseph R. Luft, Thomas D. Grant, Jayaraman Seetharaman, Gaetano T. Montelione, John F. Hunt, Edward H. Snell
"... Many bioscience fields employ high-throughput methods to screen multiple biochemical conditions. The analysis of these becomes tedious without a degree of automation. Crystallization, a rate limiting step in biological X-ray crystallography, is one of these fields. Screening of multiple potential cr ..."
Abstract - Add to MetaCart
crystallization conditions and outcome. These coefficients were evaluated against an existing similarity metric developed for crystallization, the C6 metric, using both virtual crystallization screens and by comparison of two related 1,536-cocktail high-throughput crystallization screens. Hierarchical clustering

Reliability of dimension reduction visualizations of hierarchical structures

by Elina Parviainen
"... Abstract. Dimension reduction can produce visualizations of hierarchical structures, like those produced by cluster analysis. So far, reliability of such visualizations has only been assessed with rudimentary means. Here, a method for assessing reliability of such visualizations is developed. It mea ..."
Abstract - Add to MetaCart
Abstract. Dimension reduction can produce visualizations of hierarchical structures, like those produced by cluster analysis. So far, reliability of such visualizations has only been assessed with rudimentary means. Here, a method for assessing reliability of such visualizations is developed

PSYC 579 Class Project: Analysis of Hierarchical Clustering Explorer

by Nicole Arksey , 2006
"... The Hierarchical Clustering Explorer (HCE) is an interactive visualization tool for exploring and analyzing multiple attribute datasets. The tool was developed by the Human-Computer Interaction Lab at the University of Maryland by Jinwook Seo and Ben Shneiderman (Seo and Shneiderman, 2002). The tool ..."
Abstract - Add to MetaCart
The Hierarchical Clustering Explorer (HCE) is an interactive visualization tool for exploring and analyzing multiple attribute datasets. The tool was developed by the Human-Computer Interaction Lab at the University of Maryland by Jinwook Seo and Ben Shneiderman (Seo and Shneiderman, 2002

Learning Hierarchical Linguistic Descriptions of Visual Datasets

by Roni Mittelman, Min Sun, Benjamin Kuipers, Silvio Savarese
"... We propose a method to learn succinct hierarchical linguistic descriptions of visual datasets, which allow for improved navigation efficiency in image collections. Classic exploratory data analysis methods, such as agglomerative hierarchical clustering, only provide a means of obtaining a tree-struc ..."
Abstract - Add to MetaCart
We propose a method to learn succinct hierarchical linguistic descriptions of visual datasets, which allow for improved navigation efficiency in image collections. Classic exploratory data analysis methods, such as agglomerative hierarchical clustering, only provide a means of obtaining a tree

Hissclu: a hierarchical density-based method for semi-supervised clustering

by Christian Böhm - In Proc. EDBT , 2008
"... In situations where class labels are known for a part of the objects, a cluster analysis respecting this information, i.e. semi-supervised clustering, can give insight into the class and cluster structure of a data set. Several semi-supervised clustering algorithms such as HMRF-K-Means [4], COP-K-Me ..."
Abstract - Cited by 8 (1 self) - Add to MetaCart
most consistently with the cluster structure. Using this information the hierarchical cluster structure is determined. The result is visualized in a semi-supervised cluster diagram showing both cluster structure as well as class assignment. Compared to methods based on must-links and cannot-links, our

Hierarchical Cluster Analysis of Progression Patterns in Open-Angle Glaucoma Patients With Medical Treatment

by Hyoung Won Bae , Seungsoo Rho , Hye Sun Lee , Naeun Lee , Samin Hong , Je Gong , Seong , Kyung Rim Sung , Chan Yun Kim , Chan Yun Kim , 2014
"... PURPOSE. To classify medically treated open-angle glaucoma (OAG) by the pattern of progression using hierarchical cluster analysis, and to determine OAG progression characteristics by comparing clusters. METHODS. Ninety-five eyes of 95 OAG patients who received medical treatment, and who had underg ..."
Abstract - Add to MetaCart
PURPOSE. To classify medically treated open-angle glaucoma (OAG) by the pattern of progression using hierarchical cluster analysis, and to determine OAG progression characteristics by comparing clusters. METHODS. Ninety-five eyes of 95 OAG patients who received medical treatment, and who had
Results 1 - 10 of 56