### Abstract

A scatter plot displays a relation between a pair of variables. Given a set of v variables, there are v(v -1)/2 pairs of variables, and thus the same number of possible pair wise scatter plots. Therefore for even small sets of variables, the number of scatter plots can be large. Scatter plot matrices (SPLOMs) can easily run out of pixels when presenting high-dimensional data. We introduce a theoretical method and a testbed for assessing whether our method can be used to guide interactive exploration of high-dimensional data. The method is based on nine characterizations of the 2D distributions of orthogonal pair wise projections on a set of points in multidimensional Euclidean space. Working directly with these characterizations, we can locate anomalies for further analysis or search for similar distributions in a large SPLOM with more than a hundred dimensions. Our testbed, ScagExplorer, is developed in order to evaluate the feasibility of handling huge collections of scatter plots.

Original language | English |
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Title of host publication | Proceedings - 2014 IEEE Pacific Visualization Symposium, PacificVis 2014 |

Publisher | IEEE Computer Society |

Pages | 73-80 |

Number of pages | 8 |

ISBN (Print) | 9781479928736 |

DOIs | |

State | Published - Jan 1 2014 |

Event | 2014 7th IEEE Pacific Visualization Symposium, PacificVis 2014 - Yokohama, Kanagawa, Japan Duration: Mar 4 2014 → Mar 7 2014 |

### Publication series

Name | IEEE Pacific Visualization Symposium |
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ISSN (Print) | 2165-8765 |

ISSN (Electronic) | 2165-8773 |

### Conference

Conference | 2014 7th IEEE Pacific Visualization Symposium, PacificVis 2014 |
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Country | Japan |

City | Yokohama, Kanagawa |

Period | 03/4/14 → 03/7/14 |

### Fingerprint

### Keywords

- Design Methodology-Pattern analysis
- High-Dimensional Visual Analytics
- Leader algorithm
- Scagnostics
- Scatterplot matrix
- forced-directed layout

### Cite this

*Proceedings - 2014 IEEE Pacific Visualization Symposium, PacificVis 2014*(pp. 73-80). [6787139] (IEEE Pacific Visualization Symposium). IEEE Computer Society. https://doi.org/10.1109/PacificVis.2014.42