data.frame which is used by examples in this user manual
Given is the attribute name, attribute type, the measurement unit and a brief description. The number of rings is the value to predict: either as a continuous value or as a classification problem.
Name / Data Type / Measurement Unit / Description
Id / integer / – / index of each observation
Sex / nominal / – / M, F, and I (infant)
Length / continuous / mm / Longest shell measurement
Diameter / continuous / mm / perpendicular to length
Height / continuous / mm / with meat in shell
Whole weight / continuous / grams / whole abalone
Shucked weight / continuous / grams / weight of meat
Viscera weight / continuous / grams / gut weight (after bleeding)
Shell weight / continuous / grams / after being dried
Rings / integer / – / +1.5 gives the age in years
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).
Lazy data loading is enabled in this package. So the user does not
need to explicitly run
data(abalone) to load the data. It will be
loaded whenever it is used.
 The original data is downloaded from http://archive.ics.uci.edu/ml/datasets/Abalone
 Warwick J Nash, Tracy L Sellers, Simon R Talbot, Andrew J Cawthorn and Wes B Ford (1994) "The Population Biology of Abalone (_Haliotis_ species) in Tasmania. I. Blacklip Abalone (H. rubra) from the North Coast and Islands of Bass Strait", Sea Fisheries Division, Technical Report No. 48 (ISSN 1034-3288)
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## Not run: ## Assume that .port is port number and .dbname is the database name cid <- db.connect(port = .port, dbname = .dbname, verbose = FALSE) ## create a table from the example data.frame "abalone" ## The user does not need to run data(abalone) to load the data delete("abalone", conn.id = cid) x <- as.db.data.frame(abalone, "abalone", key = "id", distributed.by = "id", conn.id = cid, verbose = FALSE) ## preview the actual data lk(x) ## preview the actual data ordered by id lk(sort(x, FALSE, x$id)) db.disconnect(cid, verbose = FALSE) ## End(Not run)
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