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A Rank-by-Feature Framework for Unsupervised Multidimensional Data Exploration Using Low Dimensional Projections (2004)
(2005)
Exploratory analysis of multidimensional data sets is challenging because of the difficulty in comprehending more than three dimensions. Two fundamental statistical principles for the exploratory analysis are (1) to examine ...
Understanding Clusters in Multidimensional Spaces: Making Meaning by Combining Insights from Coordinated Views of Domain Knowledge (2004)
(2005)
Cluster analysis of multidimensional data is widely used in many research areas including financial, economical, sociological, and biological analyses. Finding natural subclasses in a data set not only reveals interesting ...
A Rank-by-Feature Framework for Interactive Exploration of Multidimensional Data (2004)
(2005)
Interactive exploration of multidimensional data sets is challenging because: (1) it is difficult to comprehend patterns in more than three dimensions, and (2) current systems often are a patchwork of graphical and statistical ...
Building a Coherent Data Pipeline in Microarray Data Analyses: Optimization of Signal/Noise Ratios Using an Interactive Visualization Tool and a Novel Noise Filtering Method (2003)
(2005)
Motivation: Sources of uncontrolled noise strongly influence data analysis in microarray studies, yet signal/noise ratios are rarely considered in microarray data analyses. We hypothesized that different research projects ...
In vivo filtering of in vitro MyoD target data: An approach for identification of biologically relevant novel downstream targets of transcription factors (2003)
(2005)
We report a novel approach to identification of downstream targets of MyoD, where a published set of candidate targets from a well-controlled in vitro experiment [1] is filtered for relevance to muscle regeneration using ...
Interactive Exploration of Multidimensional Microarray Data: Scatterplot Ordering, Gene Ontology Browser, and Profile Search (2003)
(2005)
Multidimensional data sets are common in many research areas, including microarray experiment data sets. Genome researchers are using cluster analysis to find meaningful groups in microarray data. However, the high ...
Using Categorical Information in Multidimensional Data Sets: Interactive Partition and Cluster Comparison
(2005)
Multidimensional data sets often include categorical information. When most columns have categorical information, clustering the data set by similarity of categorical values can reveal interesting patterns in the data set. ...
Knowledge Discovery in High Dimensional Data: Case Studies and a User Survey for an Information Visualization Tool
(2005)
Knowledge discovery in high dimensional data is a challenging enterprise, but new visual analytic tools appear to offer users remarkable powers if they are ready to learn new concepts and interfaces. Our 3-year effort to ...
A Knowledge Integration Framework for Information Visualization (2004)
(2005)
Users can better understand complex data sets by combining insights from multiple coordinated visual displays that include relevant domain knowl-edge. When dealing with multidimensional data and clustering results, the ...
Understanding Hierarchical Clustering Results by Interactive Exploration of Dendrograms: A Case Study with Genomic Microarray Data (2002)
(2005)
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 ...