DataLearner is an easy-to-use tool for data mining and knowledge discovery from your own compatible ARFF and CSV-formatted training datasets. It’s fully self-contained, requires no external storage or network connectivity – it builds models directly on your phone or tablet.

>> ARFF and CSV support <<
Training datasets must be either CSV (comma-separated variable) or Weka ARFF format.
CSV files must have the following features:
* include a header row
* class attribute is initially set as last column

>> Force class attribute to nominal <<
Most of DataLearner's algorithms expect nominal/categorical class attributes and using a numeric class attribute will cause most algorithms to fail. The new 'force class attribute to nominal' feature overcomes this, however, nominal class attributes with too many distinct values may use up too much RAM.

DataLearner features classification, association and clustering algorithms from the open-source Weka (Waikato Environment for Knowledge Analysis) package, plus new algorithms developed by the Data Science Research Unit (DSRU) at Charles Sturt University. Combined, the app provides 42 machine-learning/data-mining algorithms, including RandomForest, C4.5 (J48) and NaiveBayes.

DataLearner collects no information – it requires access to your device storage simply to load your datasets and build your machine-learning models.

* DataLearner is being used as a teaching tool in the ITC573 Data and Knowledge Engineering subject for the Master of Information Technology post-graduate degree at Charles Sturt University.
* DataLearner research was presented at ADMA 2019 (15th International Conference on Advanced Data Mining and Applications) and published in 'Lecture Notes in Artificial Intelligence' (Springer)

Get the resources:
GPL3-licensed source code on Github: https://github.com/darrenyatesau/DataLearner
Quick video on YouTube: https://youtu.be/H-7pETJZf-g
Research paper on arXiv: https://arxiv.org/abs/1906.03773
AusDM 2018 conference paper that initiated DataLearner: https://www.researchgate.net/publication/331126867

Researchers, if you use this app in research applications, please cite the research papers above. Thanks.

Machine-learning algorithms include:
• Bayes – BayesNet, NaiveBayes
• Functions – Logistic, SimpleLogistic, MultiLayerPerceptron (Neural Network)
• Lazy – IBk (K Nearest Neighbours), KStar
• Meta – AdaBoostM1, Bagging, LogitBoost, MultiBoostAB, Random Committee, RandomSubSpace, RotationForest
• Rules – Conjunctive Rule, Decision Table, DTNB, JRip, OneR, PART, Ridor, ZeroR
• Trees – ADTree, BFTree, DecisionStump, ForestPA, J48 (C4.5), LADTree, Random Forest, RandomTree, REPTree, SimpleCART, SPAARC, SysFor.
• Clusterers – DBSCAN, Expectation Maximisation (EM), Farthest-First, FilteredClusterer, SimpleKMeans
• Associations – Apriori, FilteredAssociator, FPGrowth

DISCLAIMER: This software is supplied "AS-IS" - while it has been tested, no warranty or guarantee is implied or given. Use it at your own risk. Your downloading of this software shows you agree to these terms.