mushroom: Properties of 8124 mushrooms.
In mixdir: Cluster High Dimensional Categorical Datasets

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A dataset containing 23 categorical properties of 23 different species of gilled mushrooms including a categorization if it is edible or not.

mushroom

A data frame with 8124 rows and 23 columns:

bruises: bruises no
cap-color: brown yellow white gray red pink buff purple cinnamon green
cap-shape: convex bell sunken flat knobbed conical
cap-surface: smooth scaly fibrous grooves
edible: poisonous edible
gill-attachment: free attached
gill-color: black brown gray pink white chocolate purple red buff green yellow orange
gill-size: narrow broad
gill-spacing: close crowded
habitat: urban grasses meadows woods paths waste leaves
odor: pungent almond anise none foul creosote fishy spicy musty
population: scattered numerous abundant several solitary clustered
ring-number: one two none
ring-type: pendant evanescent large flaring none
spore-print-color: black brown purple chocolate white green orange yellow buff
stalk-color-above-ring: white gray pink brown buff red orange cinnamon yellow
stalk-color-below-ring: white pink gray buff brown red yellow orange cinnamon
stalk-root: equal club bulbous rooted NA
stalk-shape: enlarging tapering
stalk-surface-above-ring: smooth fibrous silky scaly
stalk-surface-below-ring: smooth fibrous scaly silky
veil-color: white brown orange yellow
veil-type: partial

The records are drawn from G. H. Lincoff (1981) (Pres.), The Audubon Society Field Guide to North American Mushrooms. New York: Alfred A. Knopf. (See pages 500–525 for the Agaricus and Lepiota Family.)

The Guide clearly states that there is no simple rule for determining the edibility of a mushroom; no rule like “leaflets three, let it be” for Poisonous Oak and Ivy.

The actual dataset from the UCI repository has been cleaned up to properly label the missing values and have the full category names instead of their abbreviations.

https://archive.ics.uci.edu/ml/datasets/Mushroom

Blake, C.L. & Merz, C.J. (1998). UCI Repository of Machine Learning Databases. Irvine, CA: University of California, Department of Information and Computer Science.