Showing 28 of total 28 results (show query)
mhahsler
arulesViz:Visualizing Association Rules and Frequent Itemsets
Extends package 'arules' with various visualization techniques for association rules and itemsets. The package also includes several interactive visualizations for rule exploration. Michael Hahsler (2017) <doi:10.32614/RJ-2017-047>.
Maintained by Michael Hahsler. Last updated 7 months ago.
arulesassociation-rulesfrequent-itemsetsinteractive-visualizationsvisualization
54 stars 11.03 score 1.7k scripts 2 dependentsmhahsler
recommenderlab:Lab for Developing and Testing Recommender Algorithms
Provides a research infrastructure to develop and evaluate collaborative filtering recommender algorithms. This includes a sparse representation for user-item matrices, many popular algorithms, top-N recommendations, and cross-validation. Hahsler (2022) <doi:10.48550/arXiv.2205.12371>.
Maintained by Michael Hahsler. Last updated 3 days ago.
collaborative-filteringrecommender-system
214 stars 10.42 score 840 scripts 2 dependents
assuom44
arlclustering:Exploring Social Network Structures Through Friendship-Driven Community Detection with Association Rules Mining
Implements an innovative approach to community detection in social networks using Association Rules Learning. The package provides tools for processing graph and rules objects, generating association rules, and detecting communities based on node interactions. Designed to facilitate advanced research in Social Network Analysis, this package leverages association rules learning for enhanced community detection. This approach is described in El-Moussaoui et al. (2021) <doi:10.1007/978-3-030-66840-2_3>.
Maintained by Mohamed El-Moussaoui. Last updated 6 months ago.
6.45 score 50 scripts
nsaph-software
CRE:Interpretable Discovery and Inference of Heterogeneous Treatment Effects
Provides a new method for interpretable heterogeneous treatment effects characterization in terms of decision rules via an extensive exploration of heterogeneity patterns by an ensemble-of-trees approach, enforcing high stability in the discovery. It relies on a two-stage pseudo-outcome regression, and it is supported by theoretical convergence guarantees. Bargagli-Stoffi, F. J., Cadei, R., Lee, K., & Dominici, F. (2023) Causal rule ensemble: Interpretable Discovery and Inference of Heterogeneous Treatment Effects. arXiv preprint <doi:10.48550/arXiv.2009.09036>.
Maintained by Falco Joannes Bargagli Stoffi. Last updated 5 months ago.
13 stars 6.41 score 11 scripts
softwaredeng
inTrees:Interpret Tree Ensembles
For tree ensembles such as random forests, regularized random forests and gradient boosted trees, this package provides functions for: extracting, measuring and pruning rules; selecting a compact rule set; summarizing rules into a learner; calculating frequent variable interactions; formatting rules in latex code. Reference: Interpreting tree ensembles with inTrees (Houtao Deng, 2019, <doi:10.1007/s41060-018-0144-8>).
Maintained by Houtao Deng. Last updated 11 months ago.
39 stars 5.85 score 72 scriptsmhahsler
arulesCBA:Classification Based on Association Rules
Provides the infrastructure for association rule-based classification including the algorithms CBA, CMAR, CPAR, C4.5, FOIL, PART, PRM, RCAR, and RIPPER to build associative classifiers. Hahsler et al (2019) <doi:10.32614/RJ-2019-048>.
Maintained by Michael Hahsler. Last updated 7 months ago.
association-rulesclassification
3 stars 5.42 score 47 scripts 1 dependents
kliegr
arc:Association Rule Classification
Implements the Classification-based on Association Rules (CBA) algorithm for association rule classification. The package, also described in Hahsler et al. (2019) <doi:10.32614/RJ-2019-048>, contains several convenience methods that allow to automatically set CBA parameters (minimum confidence, minimum support) and it also natively handles numeric attributes by integrating a pre-discretization step. The rule generation phase is handled by the 'arules' package. To further decrease the size of the CBA models produced by the 'arc' package, postprocessing by the 'qCBA' package is suggested.
Maintained by Tomas Kliegr. Last updated 7 months ago.
7 stars 5.09 score 39 scripts 1 dependents
yuanlonghu
immcp:Poly-Pharmacology Toolkit for Traditional Chinese Medicine Research
Toolkit for Poly-pharmacology Research of Traditional Chinese Medicine. Based on the biological descriptors and drug-disease interaction networks, it can analyze the potential poly-pharmacological mechanisms of Traditional Chinese Medicine and be used for drug-repositioning in Traditional Chinese Medicine.
Maintained by Yuanlong Hu. Last updated 2 years ago.
network-pharmacologypolypharmacologytraditional-chinese-medicine
5 stars 4.40 score 2 scriptskliegr
qCBA:Postprocessing of Rule Classification Models Learnt on Quantized Data
Implements the Quantitative Classification-based on Association Rules (QCBA) algorithm (<doi:10.1007/s10489-022-04370-x>). QCBA postprocesses rule classification models making them typically smaller and in some cases more accurate. Supported are 'CBA' implementations from 'rCBA', 'arulesCBA' and 'arc' packages, and 'CPAR', 'CMAR', 'FOIL2' and 'PRM' implementations from 'arulesCBA' package and 'SBRL' implementation from the 'sbrl' package. The result of the post-processing is an ordered CBA-like rule list.
Maintained by Tomáš Kliegr. Last updated 7 months ago.
11 stars 4.30 score 12 scripts
jaroslav-kuchar
rCBA:CBA Classifier for R
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).
Maintained by Jaroslav Kuchar. Last updated 6 years ago.
7 stars 4.14 score 39 scripts
bioc
TFARM:Transcription Factors Association Rules Miner
It searches for relevant associations of transcription factors with a transcription factor target, in specific genomic regions. It also allows to evaluate the Importance Index distribution of transcription factors (and combinations of transcription factors) in association rules.
Maintained by Liuba Nausicaa Martino. Last updated 5 months ago.
biologicalquestioninfrastructurestatisticalmethodtranscription
4.00 score 2 scripts
juanmartinsantos
rgnoisefilt:Elimination of Noisy Samples in Regression Datasets using Noise Filters
Traditional noise filtering methods aim at removing noisy samples from a classification dataset. This package adapts classic and recent filtering techniques for use in regression problems, and it also incorporates methods specifically designed for regression data. In order to do this, it uses approaches proposed in the specialized literature, such as Martin et al. (2021) [<doi:10.1109/ACCESS.2021.3123151>] and Arnaiz-Gonzalez et al. (2016) [<doi:10.1016/j.eswa.2015.12.046>]. Thus, the goal of the implemented noise filters is to eliminate samples with noise in regression datasets.
Maintained by Juan Martin. Last updated 1 years ago.
2 stars 4.00 score 3 scripts
nikolett0203
RulesTools:Preparing, Analyzing, and Visualizing Association Rules
Streamlines data preprocessing, analysis, and visualization for association rule mining. Designed to work with the 'arules' package, features include discretizing data frames, generating rule set intersections, and visualizing rules with heatmaps and Euler diagrams. 'RulesTools' also includes a dataset on Brook trout detection from Nolan et al. (2022) <doi:10.1007/s13412-022-00800-x>.
Maintained by Nikolett Toth. Last updated 2 months ago.
3.93 scoremhahsler
arulesNBMiner:Mining NB-Frequent Itemsets and NB-Precise Rules
NBMiner is an implementation of the model-based mining algorithm for mining NB-frequent itemsets and NB-precise rules. Michael Hahsler (2006) <doi:10.1007/s10618-005-0026-2>.
Maintained by Michael Hahsler. Last updated 3 years ago.
6 stars 3.48 score 10 scripts
cran
ibmdbR:IBM in-Database Analytics for R
Functionality required to efficiently use R with IBM(R) Db2(R) Warehouse offerings (formerly IBM dashDB(R)) and IBM Db2 for z/OS(R) in conjunction with IBM Db2 Analytics Accelerator for z/OS. Many basic and complex R operations are pushed down into the database, which removes the main memory boundary of R and allows to make full use of parallel processing in the underlying database. For executing R-functions in a multi-node environment in parallel the idaTApply() function requires the 'SparkR' package (<https://spark.apache.org/docs/latest/sparkr.html>). The optional 'ggplot2' package is needed for the plot.idaLm() function only.
Maintained by Shaikh Quader. Last updated 1 years ago.
2 stars 3.00 scorecran
discnorm:Test for Discretized Normality in Ordinal Data
Tests whether multivariate ordinal data may stem from discretizing a multivariate normal distribution. The test is described by Foldnes and Grønneberg (2019) <doi:10.1080/10705511.2019.1673168>. In addition, an adjusted polychoric correlation estimator is provided that takes marginal knowledge into account, as described by Grønneberg and Foldnes (2022) <doi:10.1037/met0000495>.
Maintained by Njål Foldnes. Last updated 3 years ago.
2.70 scoremhahsler
recommenderlabBX:Book-Crossing Dataset (BX) for 'recommenderlab'
Provides the Book-Crossing Dataset for the package recommenderlab.
Maintained by Michael Hahsler. Last updated 3 years ago.
2.70 score 1 scriptsmhahsler
recommenderlabJester:Jester Dataset for 'recommenderlab'
Provides the Jester Dataset for package recommenderlab.
Maintained by Michael Hahsler. Last updated 3 years ago.
2.70 score 1 scripts
michael-scholz-dev
clickstream:Analyzes Clickstreams Based on Markov Chains
A set of tools to read, analyze and write lists of click sequences on websites (i.e., clickstream). A click can be represented by a number, character or string. Clickstreams can be modeled as zero- (only computes occurrence probabilities), first- or higher-order Markov chains.
Maintained by Michael Scholz. Last updated 2 years ago.
12 stars 2.69 score 41 scriptscran
arulesSequences:Mining Frequent Sequences
Add-on for arules to handle and mine frequent sequences. Provides interfaces to the C++ implementation of cSPADE by Mohammed J. Zaki.
Maintained by Christian Buchta. Last updated 7 months ago.
12 stars 2.63 score
i02momuj
RKEEL:Using 'KEEL' in R Code
'KEEL' is a popular 'Java' software for a large number of different knowledge data discovery tasks. This package takes the advantages of 'KEEL' and R, allowing to use 'KEEL' algorithms in simple R code. The implemented R code layer between R and 'KEEL' makes easy both using 'KEEL' algorithms in R as implementing new algorithms for 'RKEEL' in a very simple way. It includes more than 100 algorithms for classification, regression, preprocess, association rules and imbalance learning, which allows a more complete experimentation process. For more information about 'KEEL', see <http://www.keel.es/>.
Maintained by Jose M. Moyano. Last updated 2 years ago.
2 stars 2.41 score 130 scripts
hongyuy
sbrl:Scalable Bayesian Rule Lists Model
An efficient implementation of Scalable Bayesian Rule Lists Algorithm, a competitor algorithm for decision tree algorithms; see Hongyu Yang, Cynthia Rudin, Margo Seltzer (2017) <https://proceedings.mlr.press/v70/yang17h.html>. It builds from pre-mined association rules and have a logical structure identical to a decision list or one-sided decision tree. Fully optimized over rule lists, this algorithm strikes practical balance between accuracy, interpretability, and computational speed.
Maintained by Hongyu Yang. Last updated 12 months ago.
4 stars 2.11 score 16 scripts
vijaymp38
RareComb:Combinatorial and Statistical Analyses of Rare Events
A custom implementation of the apriori algorithm and binomial tests to identify combinations of features (genes, variants etc) significantly enriched for simultaneous mutations/events from sparse Boolean input, see Vijay Kumar Pounraja, Santhosh Girirajan (2021). Version 1.1 includes a minor adjustment to the number of combinations to be considered for multiple testing correction. This updated version is more conservative in its approach and hence more selective. <doi:10.1101/2021.10.01.462832>.
Maintained by Vijay Kumar Pounraja. Last updated 3 years ago.
1.70 score 3 scripts
blansche
fdm2id:Data Mining and R Programming for Beginners
Contains functions to simplify the use of data mining methods (classification, regression, clustering, etc.), for students and beginners in R programming. Various R packages are used and wrappers are built around the main functions, to standardize the use of data mining methods (input/output): it brings a certain loss of flexibility, but also a gain of simplicity. The package name came from the French "Fouille de Données en Master 2 Informatique Décisionnelle".
Maintained by Alexandre Blansché. Last updated 2 years ago.
1 stars 1.62 score 42 scripts
cbergmeir
opusminer:OPUS Miner Algorithm for Filtered Top-k Association Discovery
Provides a simple R interface to the OPUS Miner algorithm (implemented in C++) for finding the top-k productive, non-redundant itemsets from transaction data. The OPUS Miner algorithm uses the OPUS search algorithm to efficiently discover the key associations in transaction data, in the form of self-sufficient itemsets, using either leverage or lift. See <http://i.giwebb.com/index.php/research/association-discovery/> for more information in relation to the OPUS Miner algorithm.
Maintained by Christoph Bergmeir. Last updated 5 years ago.
1 stars 1.00 score 2 scriptscran
TELP:Social Representation Theory Application: The Free Evocation of Words Technique
Using The Free Evocation of Words Technique method with some functions, this package will make a social representation and other analysis. The Free Evocation of Words Technique consists of collecting a number of words evoked by a subject facing exposure to an inducer term. The purpose of this technique is to understand the relationships created between words evoked by the individual and the inducer term. This technique is included in the theory of social representations, therefore, on the information transmitted by an individual, seeks to create a profile that define a social group.
Maintained by Gabriel Henrique Oliveira Assuncao. Last updated 2 years ago.
1.00 score
abecker-ml
GroupBN:Inferring Group Bayesian Networks using Hierarchical Feature Clustering
Group Bayesian Networks: This package implements the inference of group Bayesian networks based on hierarchical feature clustering, and the adaptive refinement of the grouping regarding an outcome of interest, as described in Becker et. al (2021) <doi: 10.1371/journal.pcbi.1008735>.
Maintained by Ann-Kristin Becker. Last updated 4 years ago.
1.00 score 1 scripts