Abalone
Tabular Classification Available

Abalone

Donated Nov 30, 1995 Biology Creative Commons Attribution 4.0 International

Abstract

Predict the age of abalone from physical measurements

Instances
4,177
Features
8
Data Type
Tabular
Missing Values
No

Purpose

Additional Information Predicting the age of abalone from physical measurements. The age of abalone is determined by cutting the shell through the cone, staining it, and counting the number of rings through a microscope -- a boring and time-consuming task. Other measurements, which are easier to obtain, are used to predict the age. Further information, such as weather patterns and location (hence food availability) may be required to solve the problem. From the original data examples with missing values were removed (the majority having the predicted value missing), and the ranges of the continuous values have been scaled for use with an ANN (by dividing by 200).

Name Role Type Description Missing
Sex Feature Categorical M, F, and I (infant) No
Length Feature Real Longest shell measurement No
Diameter Feature Real perpendicular to length No
Height Feature Real with meat in shell No
Whole Weight Feature Real whole abalone No
Shucked Weight Feature Real weight of meat No
Viscera Weight Feature Real gut weight (after bleeding) No
Shell Weight Feature Real after being dried No
Rings Target Integer +1.5 gives the age in years No

Abalone.data

CSV 187.40 KB • Data

abalone.names

MD 4.20 KB • Documentation

Index

INDEX 0.11 KB • Documentation

Papers Citing this Dataset

56 papers found

Category detection using hierarchical mean shift

By Pavan Vatturi, Weng-Keen Wong

KDD 2009 84

Using case provenance to propagate feedback to cases and adaptations

By David Leake, Scott Dial

In: Proceedings of the Ninth European Conference on Case-Based Reasoning. 2008 84

Dependence Structure Estimation via Copula

By Jian Ma, Zengqi Sun

ArXiv 2008 84

On the Selection of the Globally Optimal Prototype Subset for Nearest-Neighbor Classification

By Emilio Carrizosa, Belen Martin-Barragan, Frank Plastria, Dolores Morales

INFORMS Journal on Computing. 2007 84

Statistical tools to assess the reliability of self-organizing maps

By Eric Bodt, Marie Cottrell, Michel Verleysen

Neural Networks 15, 8-9 (2002) 967-978. 2007 84
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