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Appl. Sci. 2017, 7(12), 1200; doi:10.3390/app7121200

LPaMI: A Graph-Based Lifestyle Pattern Mining Application Using Personal Image Collections in Smartphones

Data and Knowledge Engineering Lab, Department of Computer Science and Engineering, Kyung Hee University, Suwon 446-701, Korea
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Received: 10 October 2017 / Revised: 25 November 2017 / Accepted: 17 November 2017 / Published: 27 November 2017
(This article belongs to the Section Computer Science and Electrical Engineering)

Abstract

Normally, individuals use smartphones for a variety of purposes like photography, schedule planning, playing games, and so on, apart from benefiting from the core tasks of call-making and short messaging. These services are sources of personal data generation. Therefore, any application that utilises personal data of a user from his/her smartphone is truly a great witness of his/her interests and this information can be used for various personalised services. In this paper, we present Lifestyle Pattern MIning (LPaMI), which is a personalised application for mining the lifestyle patterns of a smartphone user. LPaMI uses the personal photograph collections of a user, which reflect the day-to-day photos taken by a smartphone, to recognise scenes (called objects of interest in our work). These are then mined to discover lifestyle patterns. The uniqueness of LPaMI lies in our graph-based approach to mining the patterns of interest. Modelling of data in the form of graphs is effective in preserving the lifestyle behaviour maintained over the passage of time. Graph-modelled lifestyle data enables us to apply variety of graph mining techniques for pattern discovery. To demonstrate the effectiveness of our proposal, we have developed a prototype system for LPaMI to implement its end-to-end pipeline. We have also conducted an extensive evaluation for various phases of LPaMI using different real-world datasets. We understand that the output of LPaMI can be utilised for variety of pattern discovery application areas like trip and food recommendations, shopping, and so on. View Full-Text
Keywords: lifestyle analysis; behavioural analysis; lifestyle patterns mining; graph-based patterns discovery; objects of interest detection from images lifestyle analysis; behavioural analysis; lifestyle patterns mining; graph-based patterns discovery; objects of interest detection from images
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Khan, K.U.; Alam, A.; Dolgorsuren, B.; Uddin, M.A.; Umair, M.; Sang, U.; Duong, V.T.; Xu, W.; Lee, Y.-K. LPaMI: A Graph-Based Lifestyle Pattern Mining Application Using Personal Image Collections in Smartphones. Appl. Sci. 2017, 7, 1200.

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