價格：免費
更新日期：2019-08-03
檔案大小：5.1M
目前版本：1.1.2
版本需求：Android 4.4 以上版本
官方網站：mailto:darrenyatesau@gmail.com
DataLearner is an easy-to-use tool for data-mining and knowledge discovery from your own compatible ARFF and CSV-formatted training datasets (see below). It’s fully self-contained, requires no external storage or network connectivity – it builds models directly on your phone or tablet. In other words, this isn't a book - it's a genuine data mining app.
*** NEWS! DataLearner research has been selected for presentation at ADMA 2019 (15th International Conference on Advanced Data Mining and Applications) and will be published in 'Lecture Notes in Artificial Intelligence' (Springer) ***
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.
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
>> Training dataset file format support <<
Training datasets must conform to either the Weka ARFF format or CSV (comma-separated variable, header row expected, class attribute must be last column, class attribute will be forced to nominal/categorical).
>> Where to find ARFF files? <<
DataLearner comes with a built-in demo dataset called 'rain.csv', but you'll also find plenty of datasets at the OpenML website - including the popular 'ecoli' set (https://www.openml.org). Download the ARFF versions to your phone and load them into DataLearner to build models from. Watch our new video tutorial - https://www.youtube.com/watch?v=81tSbclMVT8
This software is supplied AS-IS - while it has been tested, no warranty is implied or given.
