Smartphones have become the ultimate 'personal' computer, yet despite this, general-purpose data mining and knowledge discovery tools for mobile devices are surprisingly rare. DataLearner is a new data mining application designed specifically for Android devices that imports the Weka data mining engine and augments it with algorithms developed by Charles Sturt University. Moreover, DataLearner can be expanded with additional algorithms. Combined, DataLearner delivers 40 classification, clustering and association rule mining algorithms for model training and evaluation without need for cloud computing resources or network connectivity. It provides the same classification accuracy as PCs and laptops, while doing so with acceptable processing speed and consuming negligible battery life. With its ability to provide easy-to-use data mining on a phone-size screen, DataLearner is a new portable, self-contained data mining tool for remote, personalised and educational applications alike. DataLearner features four elements -this paper, the app available on Google Play, the GPL3-licensed source code on GitHub and a short video on YouTube.
Data mining is the science of extracting information or ‘knowledge’ from data. It is a task commonly executed on cloud computing resources, personal computers and laptops. However, what about smartphones? Despite the fact that these ubiquitous mobile devices now offer levels of hardware and performance approaching that of laptops, locally executed model-training using data mining methods on smartphones is still notably rare. On-device model-training offers a number of advantages. It largely mitigates issues of data security and privacy, since no data is required to leave the device. It also ensures a self-contained, fully-portable data mining solution requiring no cloud computing or network resources and able to operate in any location. In this paper, we focus on the intersection of smartphones and data mining. We investigate the growth in smartphone performance, survey smartphone usage models in previous research and look at recent developments in locally-executed data mining on smartphones.
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