uci_mushroom — UCI Mushroom attributes (1981)
- Description
A bipartite network of mushroom samples and their attribute values, from the "Mushroom" dataset of the UCI Machine Learning Repository, donated by J. Schlimmer in 1987. The 8,124 records are descriptions of hypothetical samples of 23 species of gilled mushrooms of the Agaricus and Lepiota families, drawn from The Audubon Society Field Guide to North American Mushrooms (1981), each described by 22 nominal attributes of the cap, gills, stalk, veil, ring, spore print, population and habitat. Each sample is labelled as edible or poisonous, where species of unknown edibility were merged with the poisonous ones. Each sample is connected to one node for each of its attribute values (e.g. "odor=almond"). The attribute "stalk-root" is missing for 2,480 samples, which therefore have no edge for that attribute (degree 21 instead of 22).
The network has the following properties:
- Nodes:
name, the attribute value in the form "attribute=value" (empty for samples);is_attribute, whether the node is an attribute value (otherwise a mushroom sample). - Sample nodes:
poisonous, whether the sample is classified as poisonous (or of unknown edibility), otherwise it is edible;index, the position of the record in the source file (starting from 0). - Attribute nodes:
attribute, the attribute name (e.g. "cap-shape");value, the attribute value (e.g. "convex");code, the one-letter code of the value in the source file.
- Nodes:
- Tags
- Biological Unweighted Metadata
- Citation
-
- "Mushroom [Dataset]." UCI Machine Learning Repository (1981) 10.24432/C5959T Sci-Hub
- Upstream URL OK
- https://archive.ics.uci.edu/dataset/73/mushroom
- Upstream license
- CC BY 4.0, https://creativecommons.org/licenses/by/4.0/
- Networks
-
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