uci_zoo — UCI Zoo animal attributes (1990)
- Description
A bipartite network of animals and their attribute values, from the "Zoo" dataset of the UCI Machine Learning Repository, created by R. Forsyth in 1990 as an example database for his PC/BEAGLE rule-induction program. The data describe 101 animals by 15 boolean attributes (hair, feathers, eggs, milk, airborne, aquatic, predator, toothed, backbone, breathes, venomous, fins, tail, domestic, catsize) and their number of legs (0, 2, 4, 5, 6 or 8). There is one attribute node for each boolean attribute (e.g. "hair"), to which the animals that have the attribute are connected, and one node for each number of legs (e.g. "legs=4"), to which each animal with that number of legs is connected. The class of each animal (mammal, bird, etc.) is kept as a node property. The name "frog" appears twice in the source, for two records that differ only in the "venomous" attribute; both are kept as separate nodes.
The network has the following properties:
- Nodes:
name, the animal name, the attribute name, or the number of legs in the form "legs=value";is_attribute, whether the node is an attribute (otherwise an animal). - Animal nodes:
type, the class of the animal as given in the source (1-7);type_name, a name for the class (mammal, bird, reptile, fish, amphibian, insect, invertebrate); the source gives only the class numbers, and these names were assigned here from the animals listed for each class in the dataset documentation. - Attribute nodes:
attribute, the attribute name;value, its value (always 1 for the boolean attributes, the number of legs for "legs").
- Nodes:
- Tags
- Biological Unweighted Metadata
- Citation
-
- R. Forsyth, "Zoo [Dataset]." UCI Machine Learning Repository (1990) 10.24432/C5R59V Sci-Hub
- Upstream URL OK
- https://archive.ics.uci.edu/dataset/111/zoo
- Upstream license
- CC BY 4.0, https://creativecommons.org/licenses/by/4.0/
- Networks
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Tip: hover over a column header for a description.
Column legend
- $\left<k\right>$
- Mean degree
- $\sigma_k$
- Standard deviation of the degrees
- $\lambda_h$
- Largest eigenvalue of the Hashimoto (non-backtracking) matrix
- $\tau$
- Random walk mixing time
- $r$
- Degree assortativity
- $c$
- Global clustering coefficient
- $\oslash$
- (Pseudo-) diameter
- $S$
- Relative size of the largest component
- NPs
- Node properties
- EPs
- Edge properties