Multidimensional Visualization Techniques for Microarray Data

Cvek, Urška; Trutschl, Marjan; Kilgore, Phillip; Stone, Randolph; Clifford, John L.

doi:10.1109/iv.2011.37

Cited by 13 publications

(14 citation statements)

References 30 publications

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“…Overlapping and a higher number of scatter plots in a scatterplot matrix convey poor visualization [18]. Parallel coordinates are not suitable for visualizing a higher number of records [2]. When there are higher numbers of overlapping points, Radviz does not provide the expected output [2,17].…”

Section: Related Workmentioning

confidence: 99%

“…Parallel coordinates are not suitable for visualizing a higher number of records [2]. When there are higher numbers of overlapping points, Radviz does not provide the expected output [2,17]. Andrews' curves do not support visualizing clusters of multidimensional space even though they support a large number of data points [2] and require higher computational time for generating curves [19].…”

Section: Related Workmentioning

confidence: 99%

“…When there are higher numbers of overlapping points, Radviz does not provide the expected output [2,17]. Andrews' curves do not support visualizing clusters of multidimensional space even though they support a large number of data points [2] and require higher computational time for generating curves [19]. The star coordinates suffer from overlapping of clusters of variables [17,20].…”

Section: Related Workmentioning

confidence: 99%

“…However, when the numbers of available data are high, it is difficult to represent all of the data points of a scattered dataset as a plot due to the high number of overlapping data points, also called occlusion or over-plotting. This is one of the main issues in the field of data visualization, which leads to the loss of data in projection [2]. On the other hand, when the numbers of variables are high, special techniques are required to represent multi-dimensions on a two-dimensional or three-dimensional space.…”

Section: Introductionmentioning

confidence: 99%

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Multi-Variable, Multi-Layer Graphical Knowledge Unit for Storing and Representing Density Clusters of Multi-Dimensional Big Data

et al. 2016

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A multi-variable visualization technique on a 2D bitmap for big data is introduced. If A and B are two data points that are represented using two similar shapes with m pixels, where each shape is colored with RGB color of (0, 0, k), when A X B ‰ F, adding the color of A X B gives higher color as (0, 0, 2k) and the highlight as a high density cluster, where RGB stands for Red, Green, Blue and k is the blue color. This is the hypothesis behind the single variable graphical knowledge unit (GKU), which uses the entire bit range of a pixel for a single variable. Instead, the available bit range of a pixel is split, and a pixel can be used for representing multiple variables (multi-variables). However, this will limit the bit block for single variables and limit the amount of overlapping. Using the same size k (>1) bitmaps (multi-layers) will increase the number of bits per variable (BPV), where each (x, y) of an individual layer represents the same data point. Then, one pixel in a four-layer GKU is capable of showing more than four billion overlapping ones when BPV = 8 bits (2 (BPVˆnumber of layers) ) Then, the 32-bit pixel format allows the representation of a maximum of up to four dependent variables against one independent variable. Then, a four-layer GKU of w width and h height has the capacity of representing a maximum of (2 (BPVˆnumber of layers) )ˆmˆwˆh overlapping occurrences.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Multi-Variable, Multi-Layer Graphical Knowledge Unit for Storing and Representing Density Clusters of Multi-Dimensional Big Data

et al. 2016

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“…al [6] uses multiple visualization techniques to explore the hidden relationship among data. But they does not use as interaction mechanism.…”

Section: International Journal Of Computer Applications (0975 -8887) mentioning

confidence: 99%

Circular Visualization Enhancement through Complementary Interaction

Makwana¹,

Tanwani²,

Jain³

2013

IJCA

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In today's scenario, most of the applications like web repository generate bulk of heterogeneous data. Visualization helps in the interpretation of meaningful information from huge data generated by these applications. There are many visualization techniques available in literature. Parallel Coordinates, Glyph and Projection techniques are some of the famous data visualization techniques. Projection techniques are unable to illustrate the details of data like inter-tuple variations. Parallel Coordinates have good capacity to express the details, but require more space as well as it is affected from clutter. Trend figure is also affected from clutter up to some extent. Considering the heterogeneous nature of data and limitation of visualization techniques, single visualization technique does not fulfill the analytical requirements of different application domains. But combination of these visualization techniques may fulfill the analytical requirements of applications from different domain. In this work, we have developed a tool, in which Parallel coordinates and trend figure used as interactive techniques with projection technique. The proposed combination reduces the limitation like inter-tuple variations of projection technique. Analysis of data with proposed tool has the potential to uncover other relationships and non-trivial structures.

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Relationship Discovery and Navigation in Big Graphs

Mojžiš

Laclavik

2015

Studies in Computational Intelligence

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Multidimensional Visualization Techniques for Microarray Data

Cited by 13 publications

References 30 publications

Multi-Variable, Multi-Layer Graphical Knowledge Unit for Storing and Representing Density Clusters of Multi-Dimensional Big Data

Multi-Variable, Multi-Layer Graphical Knowledge Unit for Storing and Representing Density Clusters of Multi-Dimensional Big Data

Circular Visualization Enhancement through Complementary Interaction

Relationship Discovery and Navigation in Big Graphs

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