Implementation and performance analysis of data mining classification algorithms on smartphones

Research output: Book chapter/Published conference paperConference paperpeer-review

2 Citations (Scopus)

Abstract

Smartphones are increasingly being used to capture data and perform complex tasks, however, this rarely extends to the local training of data models.This study investigates the implementation of data mining classification algorithms on smartphones, using 20 classifiers and nine mixed-design datasets on three devices. Their accuracy and processing speed are further compared against a laptop computer using our cross-platform ‘DataBench’ software. Results show that smartphones not only deliver classification accuracy equal to that of more powerful computers when using the same algorithms, as expected, but also, as many as 75% of the 180 algorithm/dataset learning tasks tested were completed on smartphone hardware within three seconds. However, tests further show that the increased complexity of newer algorithms searching forever greater classification accuracy is resulting in model-build times growing at an exponential rate. Additional testing identified that while a single algorithm execution can have negligible effect on battery life, power efficiency is affected by algorithm complexity, data size and attribute type. The increased processing demand of local model-learning on smartphones also results in increased power dissipation. Yet, even on a continuous-loop execution basis, mobile temperaturegains over a 15-minute period did not exceed 7°C. Our conclusion is that smartphones are ready to form self-reliant mobile data-mining solutions able to efficiently execute a wide range of classification algorithms. This offers numerous advantages, including data security and privacy improvements, removal of reliance on network connectivity and delivery of personalised learning.
Original languageEnglish
Title of host publicationData Mining
Subtitle of host publication16th Australasian Conference, AusDM 2018 Bahrurst, NSW, Australia, November 28–30, 2018 Revised Selected Papers
EditorsRafiqul Islam, Yun Sing Koh, Yanchang Zhao, Graco Warwick, David Stirling, Chang-Tsun Li, Zuhidul Islam
Place of PublicationSingapore
PublisherSpringer
Pages331-343
Number of pages13
Volume996
ISBN (Electronic)9789811366611
ISBN (Print)9789811366604
DOIs
Publication statusPublished - 16 Feb 2019
Event16th Australasian Data Mining Conference (AusDM 2018) - Charles Sturt University, Bathurst, Australia
Duration: 28 Nov 201830 Nov 2018
https://www.springer.com/us/book/9789811366604 (link to conf proceedings book)
https://ausdm18.ausdm.org/ (conference website)

Conference

Conference16th Australasian Data Mining Conference (AusDM 2018)
Country/TerritoryAustralia
CityBathurst
Period28/11/1830/11/18
OtherThe Australasian Data Mining Conference (AusDM) has established itself as the premier Australasian meeting for both practitioners and researchers in data mining. It is devoted to the art and science of intelligent analysis of (usually big) data sets for meaningful (and previously unknown) insights. This conference will enable the sharing and learning of research and progress in the local context and new breakthroughs in data mining algorithms and their applications across all industries.
Internet address

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