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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 article, 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.<\/jats:p>","DOI":"10.1145\/3529753","type":"journal-article","created":{"date-parts":[[2022,4,22]],"date-time":"2022-04-22T15:28:19Z","timestamp":1650641299000},"page":"1-38","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":19,"title":["Data Mining on Smartphones: An Introduction and Survey"],"prefix":"10.1145","volume":"55","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0438-3585","authenticated-orcid":false,"given":"Darren","family":"Yates","sequence":"first","affiliation":[{"name":"Charles Sturt University, Bathurst, NSW, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4868-4945","authenticated-orcid":false,"given":"Md Zahidul","family":"Islam","sequence":"additional","affiliation":[{"name":"Charles Sturt University, Bathurst, NSW, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2022,12,3]]},"reference":[{"key":"e_1_3_2_2_2","unstructured":"Dan Ackerman. 2007. 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