| Title: | CBA Classifier |
| Version: | 0.4.3 |
| Author: | Jaroslav Kuchar [aut, cre], Tomas Kliegr [ctb] |
| Maintainer: | Jaroslav Kuchar <jaroslav.kuchar@gmail.com> |
| URL: | https://github.com/jaroslav-kuchar/rCBA |
| BugReports: | https://github.com/jaroslav-kuchar/rCBA/issues |
| Description: | Provides implementations of a classifier based on the "Classification Based on Associations" (CBA). It can be used for building classification models from association rules. Rules are pruned in the order of precedence given by the sort criteria and a default rule is added. The final classifier labels provided instances. CBA was originally proposed by Liu, B. Hsu, W. and Ma, Y. Integrating Classification and Association Rule Mining. Proceedings KDD-98, New York, 27-31 August. AAAI. pp80-86 (1998, ISBN:1-57735-070-7). |
| Depends: | R (≥ 3.1.3), rJava, arules |
| Imports: | R.utils, TunePareto, methods, stats, utils |
| License: | Apache License (== 2.0) |
| LazyData: | true |
| SystemRequirements: | Java (>= 8) |
| RoxygenNote: | 6.1.1 |
| Encoding: | UTF-8 |
| Collate: | 'init.R' 'build.R' 'buildFPGrowth.R' 'classification.R' 'fpgrowth.R' 'pruning.R' 'utils.R' |
| NeedsCompilation: | no |
| Packaged: | 2019-05-29 21:21:38 UTC; jaroslav.kuchar |
| Repository: | CRAN |
| Date/Publication: | 2019-05-29 21:50:03 UTC |
Build classifier function (Apriori-based)
Description
Automatic build of the classification model using the Apriori algorithm from the arules
Usage
build(trainData, className = NA, pruning = TRUE, sa = list(),
verbose = TRUE, parallel = TRUE)
Arguments
trainData |
|
className |
column name with the target class - default is the last column |
pruning |
performing pruning while building the model |
sa |
simulated annealing setting. Default values: list(temp=100.0, alpha=0.05, tabuRuleLength=5, timeout=10) |
verbose |
verbose indicator |
parallel |
parallel indicator |
Value
list with parameters and model as data.frame with rules
Examples
library("rCBA")
data("iris")
output <- rCBA::build(iris,sa = list(alpha=0.5), parallel=FALSE) # speeding up the cooling
model <- output$model
predictions <- rCBA::classification(iris, model)
table(predictions)
sum(as.character(iris$Species)==as.character(predictions), na.rm=TRUE) / length(predictions)
Build classifier function (FP-Growth-based)
Description
Automatic build of the classification model using the FP-Growth algorithm
Usage
buildFPGrowth(train, className = NULL, verbose = TRUE,
parallel = TRUE)
Arguments
train |
|
className |
column name with the target class - default is the last column |
verbose |
verbose indicator |
parallel |
parallel indicator |
Value
list with parameters and model as data.frame with rules
Examples
library("rCBA")
data("iris")
output <- rCBA::buildFPGrowth(iris[sample(nrow(iris), 10),], "Species",
parallel=FALSE, verbose=TRUE)
inspect(output$model)
A classification function
Description
A classification function
Usage
classification(test, rules, verbose = TRUE)
Arguments
test |
|
rules |
|
verbose |
verbose indicator |
Value
vector with classifications
Examples
library("arules")
library("rCBA")
data("iris")
train <- sapply(iris, as.factor)
train <- data.frame(train, check.names=FALSE)
txns <- as(train,"transactions")
rules = apriori(txns, parameter=list(support=0.03, confidence=0.03, minlen=2),
appearance = list(rhs=c("Species=setosa", "Species=versicolor", "Species=virginica"),default="lhs"))
predictions <- rCBA::classification(train,rules)
table(predictions)
sum(as.character(train$Species)==as.character(predictions),na.rm=TRUE)/length(predictions)
FP-Growth
Description
FP-Growth algorithm - Jiawei Han, Jian Pei, and Yiwen Yin. Mining frequent patterns without candidate generation. SIGMOD Rec. 29, 2 (2000) <doi:10.1145/335191.335372>
Usage
fpgrowth(train, support = 0.01, confidence = 1, maxLength = 5,
consequent = NULL, verbose = TRUE, parallel = TRUE)
Arguments
train |
|
support |
minimum support |
confidence |
minimum confidence |
maxLength |
maximum length |
consequent |
filter consequent - column name with consequent/target class |
verbose |
verbose indicator |
parallel |
parallel indicator |
Examples
library("rCBA")
data("iris")
train <- sapply(iris,as.factor)
train <- data.frame(train, check.names=FALSE)
txns <- as(train,"transactions")
rules = rCBA::fpgrowth(txns, support=0.03, confidence=0.03, maxLength=2, consequent="Species",
parallel=FALSE)
predictions <- rCBA::classification(train,rules)
table(predictions)
sum(as.character(train$Species)==as.character(predictions),na.rm=TRUE)/length(predictions)
prunedRules <- rCBA::pruning(train, rules, method="m2cba", parallel=FALSE)
predictions <- rCBA::classification(train, prunedRules)
table(predictions)
sum(as.character(train$Species)==as.character(predictions),na.rm=TRUE)/length(predictions)
Conversion of data.frame to rules from arules
Description
Conversion of data.frame to rules from arules
Usage
frameToRules(model)
Arguments
model |
|
Value
arules rules representation
Examples
library("rCBA")
model <- data.frame("rules" = c("{X=1} => {Y=1}","{X=0} => {Y=0}"),
"support" = c(0.5,0.5),
"confidence" = c(0.5,0.5),
"lift" = c(1.0,1.0))
rules <- rCBA::frameToRules(model)
inspect(rules)
A Pruning function
Description
A Pruning function
Usage
pruning(train, rules, method = "m2cba", verbose = TRUE,
parallel = TRUE)
Arguments
train |
trainData |
rules |
|
method |
pruning method m2cba(default)|m1cba|dcbrcba |
verbose |
verbose indicator |
parallel |
parallel indicator |
Value
data.frame with pruned rules
Examples
library("arules")
library("rCBA")
data("iris")
train <- sapply(iris,as.factor)
train <- data.frame(train, check.names=FALSE)
txns <- as(train,"transactions")
rules = apriori(txns, parameter=list(support=0.03, confidence=0.03, minlen=2),
appearance = list(rhs=c("Species=setosa", "Species=versicolor", "Species=virginica"),default="lhs"))
print(length(rules))
prunedRules <- rCBA::pruning(train, rules, method="m2cba", parallel=FALSE)
print(length(prunedRules))