Implementation and Performance Analysis of Data-Mining Classification Algorithms on Smartphones

Yates, Darren; Islam, Zahidul; Gao, Junbin

doi:10.1007/978-981-13-6661-1_26

Cited by 4 publications

(2 citation statements)

References 7 publications

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“…A paper published 2019 by Yates et al [17] evaluates the performance of various data mining algorithms on mobile devices. The authors have imported a Weka [18] jar archive into an Android project.…”

Section: A Contributionsmentioning

confidence: 99%

GPU backed Data Mining on Android Devices

Robert¹,

Plant²

2021

Preprint

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Choosing an appropriate programming paradigm for high-performance computing on low-power devices can be useful to speed up calculations. Many Android devices have an integrated GPU and -although not officially supportedthe OpenCL framework can be used on Android devices for addressing these GPUs. OpenCL supports thread and data parallelism. Applications that use the GPU must account for the fact that they can be suspended by the user or the Android operating system at any moment.We have created a wrapper library that allows to use OpenCL on Android devices. Already written OpenCL programs can be executed with almost no modification. We have used this library to compare the performance of the DBSCAN and Kmeans algorithms on an integrated GPU of an Arm-v7 tablet with other single and multithreaded implementations on the same device. We have investigated which programming paradigm and language allows the best tradeoff between execution speed and energy consumption.Using the GPU for HPC on Android devices can help to carry out computationally intensive machine learning or data mining tasks in remote areas, under harsh environmental conditions and in areas where energy supply is an issue.

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Section: A Contributionsmentioning

confidence: 99%

GPU backed Data Mining on Android Devices

Robert¹,

Plant²

2021

Preprint

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“…Previous research we have conducted has found that the Weka core engine is operable within the Android platform [13]. That research revealed through early testing that Android smartphones present no issues in terms of classification accuracy, delivering the same accuracy performance as personal computers (PCs).…”

Section: Related Workmentioning

confidence: 99%

DataLearner: A Data Mining and Knowledge Discovery Tool for Android Smartphones and Tablets

Yates

Islam

Gao

2019

Advanced Data Mining and Applications

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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.

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Readiness of Smartphones for Data Collection and Data Mining with an Example Application in Mental Health

Yates

Islam

2019

Communications in Computer and Information Science

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Implementation and Performance Analysis of Data-Mining Classification Algorithms on Smartphones

Cited by 4 publications

References 7 publications

GPU backed Data Mining on Android Devices

GPU backed Data Mining on Android Devices

DataLearner: A Data Mining and Knowledge Discovery Tool for Android Smartphones and Tablets

Readiness of Smartphones for Data Collection and Data Mining with an Example Application in Mental Health

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