wine (Machine Learning Data)
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This network is in the collection of Machine Learning Data
wine is
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Metadata
| Name | Wine |
| Data types | Multivariate |
| Data task | Classification |
| Attribute types | Integer, Real |
| Instances | 178 |
| Attributes | 13 |
| Year | 1991 |
| Area | Physical |
| Description | Using chemical analysis determine the origin of wines |
Please cite the following if you use the data:
Note that if you transform/preprocess the data, please consider sharing the data by uploading it along with the details on the transformation and reference to any published materials using it.
@inproceedings{nr,
title={The Network Data Repository with Interactive Graph Analytics and Visualization},
author={Ryan A. Rossi and Nesreen K. Ahmed},
booktitle={AAAI},
url={http://networkrepository.com},
year={2015}
}
@ Name = WineData types = MultivariateData task = ClassificationAttribute types = Integer,
RealInstances = 178Attributes = 13Year = 1991Area = PhysicalDescription = Using chemical analysis determine the origin of wines,
Interactive Visualization of Node-level Properties and Statistics
Tools for Interactive Exploration of Node-level Statistics
Visualize and interactively explore wine and its important node-level statistics!
- Each point represents a node (vertex) in the graph.
- A subset of interesting nodes may be selected and their properties may be visualized across all node-level statistics. To select a subset of nodes, hold down the left mouse button while dragging the mouse in any direction until the nodes of interest are highlighted.This feature allows users to explore and analyze various subsets of nodes and their important interesting statistics and properties to gain insights into the graph data
- Zoom in/out on the visualization you created at any point by using the buttons below on the left.
- Once a subset of interesting nodes are selected, the user may further analyze by selecting and drilling down on any of the interesting properties using the left menu below.
- We also have tools for interactively visualizing, comparing, and exploring the graph-level properties and statistics.