Individualized Time-Series Segmentation for Mining Mobile Phone User Behavior
| Autor: | Muhammad Ashad Kabir, Iqbal H. Sarker, Alan Colman, Jun Han |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Machine Learning General Computer Science Computer science Machine Learning (stat.ML) 02 engineering and technology Interval (mathematics) computer.software_genre Machine learning Machine Learning (cs.LG) Personalization Computer Science - Computers and Society Statistics - Machine Learning Time-series segmentation Computers and Society (cs.CY) 0202 electrical engineering electronic engineering information engineering Segmentation Multimedia business.industry 020206 networking & telecommunications Mobile data mining Time optimal Behavioral data Mobile phone 020201 artificial intelligence & image processing Artificial intelligence business computer |
| Zdroj: | The Computer Journal. 61:349-368 |
| ISSN: | 1460-2067 0010-4620 |
| DOI: | 10.1093/comjnl/bxx082 |
| Popis: | Mobile phones can record individual's daily behavioral data as a time-series. In this paper, we present an effective time-series segmentation technique that extracts optimal time segments of individual's similar behavioral characteristics utilizing their mobile phone data. One of the determinants of an individual's behavior is the various activities undertaken at various times-of-the-day and days-of-the-week. In many cases, such behavior will follow temporal patterns. Currently, researchers use either equal or unequal interval-based segmentation of time for mining mobile phone users' behavior. Most of them take into account static temporal coverage of 24-h-a-day and few of them take into account the number of incidences in time-series data. However, such segmentations do not necessarily map to the patterns of individual user activity and subsequent behavior because of not taking into account the diverse behaviors of individuals over time-of-the-week. Therefore, we propose a behavior-oriented time segmentation (BOTS) technique that takes into account not only the temporal coverage of the week but also the number of incidences of diverse behaviors dynamically for producing similar behavioral time segments over the week utilizing time-series data. Experiments on the real mobile phone datasets show that our proposed segmentation technique better captures the user's dominant behavior at various times-of-the-day and days-of-the-week enabling the generation of high confidence temporal rules in order to mine individual mobile phone users' behavior. 20 pages |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|