*** Some news... DataLearner has been accepted for presentation at ADMA 2019 (International Conference on Advanced Data Mining and Applications) and will be published in 'Lecture Notes in Artificial Intelligence' (Springer) ***
DataLearner is an open-source easy-to-use tool for data mining and knowledge discovery from your own compatible training datasets. It’s fully self-contained, requires no external storage or network connectivity – it builds machine-learning models directly on your phone or tablet.
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 currently provides 40 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 requested models.
App on Google Play: https://play.google.com/store/apps/details?id=au.com.darrenyates.datalearner
Short video tutorial on YouTube: https://youtu.be/H-7pETJZf-g
Research paper on arXiv: https://arxiv.org/abs/1906.03773
Researchers: if you use this work, please cite the research paper above. Thanks.
Algorithms include:
• Bayes – BayesNet, NaiveBayes
• Functions – Logistic, SimpleLogistic, MultilayerPerceptron
• 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
Training datasets must conform to the Weka ARFF or standard CSV file format (CSV requires header row, class attribute is last column and will be configured as nominal).
To use CSV files in DataLearner, the file MUST include the following: 1. header row 2. class attribute must be the last column in the CSV table 3. class attribute will be forced to 'nominal' - that means the class values will be considered 'categorical' or 'nominal'. (points 2 and 3 may change once we're sure that it works reliably). If you wish to report a bug in DataLearner, please don't just say 'your app is rubbish - it crashed' - it might make you feel better, but it won't help us fix it. Instead, tell us:* the number of attributes and records in your dataset,
* the approximate file size,
* the algorithm you were attempting to use, and,
* the device you're attempting to do this on.
From that, we can get to work and see what the problem was. While we've tested the app as thoroughly as we could, we can't claim to have found every possible way to crash an Android app. At time of writing, there was no other app on Google Play like DataLearner, so we're discovering new things about locally-executed data-mining all the time. DataLearner's privacy policy is pretty simple:
1. DataLearner does not collect or transmit any user data. The only data it uses is the training datasets you provide in ARFF or CSV files (otherwise, it doesn't do anything).
2. DataLearner does not save any data, including any data regarding any training dataset you load for data-mining. Any training data remaining in RAM after use can be erased by just rebooting your phone.
(Google Play collects general app download and usage analytics about DataLearner, which we use to correct bugs and issues, but this is no different to any other app on Google Play. We have no control over this.)
By installing and using the app, you agree to this policy. Cheers!