196,862 research outputs found
Fast training of self organizing maps for the visual exploration of molecular compounds
Visual exploration of scientific data in life science
area is a growing research field due to the large amount of
available data. The Kohonen’s Self Organizing Map (SOM) is
a widely used tool for visualization of multidimensional data.
In this paper we present a fast learning algorithm for SOMs
that uses a simulated annealing method to adapt the learning
parameters. The algorithm has been adopted in a data analysis
framework for the generation of similarity maps. Such maps
provide an effective tool for the visual exploration of large and
multi-dimensional input spaces. The approach has been applied
to data generated during the High Throughput Screening
of molecular compounds; the generated maps allow a visual
exploration of molecules with similar topological properties.
The experimental analysis on real world data from the
National Cancer Institute shows the speed up of the proposed
SOM training process in comparison to a traditional approach.
The resulting visual landscape groups molecules with similar
chemical properties in densely connected regions
An algorithm for quantifying dependence in multivariate data sets
We describe an algorithm to quantify dependence in a multivariate data set.
The algorithm is able to identify any linear and non-linear dependence in the
data set by performing a hypothesis test for two variables being independent.
As a result we obtain a reliable measure of dependence.
In high energy physics understanding dependencies is especially important in
multidimensional maximum likelihood analyses. We therefore describe the problem
of a multidimensional maximum likelihood analysis applied on a multivariate
data set with variables that are dependent on each other. We review common
procedures used in high energy physics and show that general dependence is not
the same as linear correlation and discuss their limitations in practical
application.
Finally we present the tool CAT, which is able to perform all reviewed
methods in a fully automatic mode and creates an analysis report document with
numeric results and visual review.Comment: 4 pages, 3 figure
Multidimensional Analysis: A Video Based Case Study Research Methodology for Examining Individual Dance/Movement Therapy Sessions
Multidimensional Analysis, a video based case study research methodology, was created by this author to examine multivariable qualitative data and develop an understanding of the therapeutic value and relational characteristics of auditory, visual and contextual components in individual dance/movement therapy sessions. The purpose of this study is to evaluate the benefits and limitations of Multidimensional Analysis based on its development and use in a preliminary study. Multidimensional Analysis involved an examination of individual dance/movement therapy sessions as a whole, as differentiated moments, and again as a whole. Videotaping each session was the primary form of data collection from which all other data collection and analysis procedures originated. This methodology was beneficial in broadening the researcher’s perspective and understanding of the auditory, visual and contextual components of the videotaped sessions, but also resulted in complex information that was difficult to process despite time consuming analysis procedures. Suggestions for modifying Multidimensional Analysis for future use in dance/movement therapy research are discussed, as well as implications for practicing dance/movement therapists
Cluster Oriented Spatio Temporal Multidimensional Data Visualization of Earthquakes in Indonesia
Spatio temporal data clustering is challenge task. The result of clustering data are utilized to investigate the seismic parameters. Seismic parameters are used to describe the characteristics of earthquake behavior. One of the effective technique to study multidimensional spatio temporal data is visualization. But, visualization of multidimensional data is complicated problem. Because, this analysis consists of observed data cluster and seismic parameters. In this paper, we propose a visualization system, called as IES (Indonesia Earthquake System), for cluster analysis, spatio temporal analysis, and visualize the multidimensional data of seismic parameters. We analyze the cluster analysis by using automatic clustering, that consists of get optimal number of cluster and Hierarchical K-means clustering. We explore the visual cluster and multidimensional data in low dimensional space visualization. We made experiment with observed data, that consists of seismic data around Indonesian archipelago during 2004 to 2014.Keywords: Clustering, visualization, multidimensional data, seismic parameters
mTreeIllustrator: A Mixed-Initiative Framework for Visual Exploratory Analysis of Multidimensional Hierarchical Data
Multidimensional hierarchical (mTree) data are very common in daily life and scientific research. However, mTree data exploration is a laborious and time-consuming process due to its structural complexity and large dimension combination space. To address this problem, we present mTreeIllustrator, a mixed-initiative framework for exploratory analysis of multidimensional hierarchical data with faceted visualizations. First, we propose a recommendation pipeline for the automatic selection and visual representation of important subspaces of mTree data. Furthermore, we design a visual framework and an interaction schema to couple automatic recommendations with human specifications to facilitate progressive exploratory analysis. Comparative experiments and user studies demonstrate the usability and effectiveness of our framework
Visual Analysis of Microarray Data from Bioinformatics Applications
We present a new application designed for the visual exploration of microarray data.It is based on an extension and adaption of parallel coordinates to
support the visual exploration of large and high-dimensional datasets. In particular, we investigate the visual analysis of gene-expression data as generated by microarray experiments. We combine refined visual exploration with statistical methods to a visual analytics approach, which proved to be particularly successful in this application domain. We will demonstrate the usefulness on several multidimensional gene-expression datasets from different bioinformatics applications
TICAL - a web-tool for multivariate image clustering and data topology preserving visualization
In life science research bioimaging is often used to study two kinds of features in a sample simultaneously: morphology and co-location of molecular components. While bioimaging technology is rapidly proposing and improving new multidimensional imaging platforms, bioimage informatics has to keep pace in order to develop algorithmic approaches to support biology experts in the complex task of data analysis. One particular problem is the availability and applicability of sophisticated image analysis algorithms via the web so different users can apply the same algorithms to their data (sometimes even to the same data to get the same results) and independently from her/his whereabouts and from the technical features of her/his computer. In this paper we describe TICAL, a visual data mining approach to multivariate microscopy analysis which can be applied fully through the web.We describe the algorithmic approach, the software concept and present results obtained for different example images
Visual decisions in the analysis of customers online shopping behavior
The analysis of the online customer shopping behavior is an important task nowadays, which allows maximizing the efficiency of advertising campaigns and increasing the return of investment for advertisers. The analysis results of online customer shopping behavior are usually reviewed and understood by a non-technical person; therefore the results must be displayed in the easiest possible way. The online shopping data is multidimensional and consists of both numerical and categorical data. In this paper, an approach has been proposed for the visual analysis of the online shopping data and their relevance. It integrates several multidimensional data visualization methods of different nature. The results of the visual analysis of numerical data are combined with the categorical data values. Based on the visualization results, the decisions on the advertising campaign could be taken in order to increase the return of investment and attract more customers to buy in the online e-shop
Quaternion-based complexity study of human postural sway time series
A multidimensional approach for the study of the center of pressure (CoP) was selected. During the work the dataset was characterized recurring to algorithms taken from Chaotic and Stochastic time series analysis. The effects of the visual and cognitive components were addressed to allow a proper modelization of the data in the complex and quaternion domains
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