An implementation using an algorithm based on a Depth-First expansion and BitMaps database representation. Caren was developed with the purpose of deriving
Classification models. CAREN generates association rules for attribute/value and basket
datasets. Caren implements
three different methods for dealing with numeric attributes: binary, class intervals and Srikant
discretization. It also implements different metrics for rules filtering
and several formats for rules output. The package includes a classifier ($predict$) that uses prediction models
made out of rules generated from the Caren engine. Caren derives and handles classification, numeric prediction and recommendation models.
A module for pre-processing numeric attributes is also included ($convert$).
An improved depth-first expansion implementation of Caren is available. It is an enhanced version with more features.
This version includes a new schema for representing rules and prediction models. The overal association rules engine was improved.
New features include: Ensemble methods like Post-bagging and Iterative Reordering,
Distribution rules, numeric prediction models using these rules (regression rules),
rule selection using Fisher Exact Test,
a new and optimized implementation of the improvement filter and several other performance speedups. It also implements Webb's Bonferroni-like layered critical values adjustment to
cope with multiple hypothesis testing.
For each association rule, Caren derives 13 distinct interest measures (confidence, lift, conviction, chi-squared statistics, laplace, leverage, jaccard, cosine, phi, mutual information, weighted relative accuracy, entropy and gini.
Caren now includes a proposal to derive RULES describing CONTRAST SETS to detect the differences between contrasting groups.
The package now includes two versions of a C-shell scripts for performing N-cross validation.
Folders are derived using WEKA stratified fold generation methods.
Read files "README-caren_command" for details.
The new caren 2.6 version is now released. It includes a truly rule based algorithm, new pruning filters, a contrast sets algorithm, a CMAR algorithm implementation, Jittering ensembles implementation, among several other features.
Old versions of caren
Developed under the FCT - POSI/2001 project CLASS -
(Classificação e Associação usando Regras de
Associação).
Download:
Contrast Sets datasets stucco and rcs
The new CAREN2.6.4 system (beta version updated 9/10/2017).
The CAREN2.6 system.
The CAREN2.5.2 system (updated 19/11/2009).
CARENCLASS2.5 system (21/02/2008).
CARENCLASS2.4.2 system (4/09/2007).
CARENCLASS2.4 system.
CARENCLASS2.3 system.
Reports:
Technical report describing our Apriori implementation (old version).
Technical report describing the Predict module and the new version of the Caren implementation.