Package index

Package Overview

Learn about the package and its association rule-based classification infrastructure.

arulesCBA arulesCBA-package

arulesCBA: Classification Based on Association Rules

Data Preparation and Rule Mining

Discretize data, convert it to transactions, and mine class association rules for classification.

prepareTransactions()

Prepare Data for Associative Classification

discretizeDF.supervised()

Supervised Methods to Convert Continuous Variables into Categorical Variables

mineCARs()

Mine Class Association Rules

transactions2DF()

Convert Transactions to a Data Frame

Classifiers

Build classifiers from association rules using CBA and alternative rule-learning algorithms.

CBA() pruneCBA_M1() pruneCBA_M2()

Classification Based on Association Rules Algorithm (CBA)

CBA_ruleset()

Constructor for Objects for Classifiers Based on Association Rules

RCAR()

Regularized Class Association Rules for Multi-class Problems (RCAR+)

FOIL()

Use FOIL to learn a rule set for classification

FOIL2() CPAR() PRM() CMAR()

Interface to the LUCS-KDD Implementations of CMAR, PRM and CPAR

RIPPER_CBA() PART_CBA() C4.5_CBA()

CBA classifiers based on rule-based classifiers in RWeka

predict(<CBA>) accuracy()

Model Prediction for Classifiers Based on Association Rules

Prediction and Evaluation

Predict class labels for new objects and evaluate classifiers.

predict(<CBA>) accuracy()

Model Prediction for Classifiers Based on Association Rules

Utilities

Extract classes and responses, analyze rule coverage, and determine default classes.

classes() response() classFrequency() majorityClass() transactionCoverage() uncoveredClassExamples() uncoveredMajorityClass()

Helper Functions for Dealing with Classes

Data Sets

Example classification data sets from the UCI Machine Learning Repository.

Lymphography

The Lymphography Domain Data Set (UCI)

Mushroom

The Mushroom Data Set (UCI)