Automatic detection of points of interest using spatio-temporal data mining
Location Based Services (LBS) are still in their infancy but they are evolving rapidly. It is expected to have more intelligent, adaptive and predictive LBS applications in the future, which can detect users’ intentions and understand their needs, demands and responses. To have such intelligent serv...
| Main Authors: | , , , |
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| Format: | Article |
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Rinton Press
2015
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| Online Access: | https://eprints.nottingham.ac.uk/35034/ |
| _version_ | 1848794987674730496 |
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| author | Basiri, Anahid Marsh, Stuart Moore, Terry Amiran, Pouria |
| author_facet | Basiri, Anahid Marsh, Stuart Moore, Terry Amiran, Pouria |
| author_sort | Basiri, Anahid |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | Location Based Services (LBS) are still in their infancy but they are evolving rapidly. It is expected to have more intelligent, adaptive and predictive LBS applications in the future, which can detect users’ intentions and understand their needs, demands and responses. To have such intelligent services, LBS applications should be able to understand users’ behaviours, preferences and interests automatically and without needing users to be asked to specify them. Then, using users’ current situations and previously extracted behaviours, interests and preferences, LBS applications could provide the most appropriate sets of services. This paper shows the application of data mining techniques over anonymous sets of tracking data to recognise mobility behaviours and extract some navigational user preferences such as Point of Interests (PoI) in a format of if-then rules, spatial patterns, models and knowledge. Such knowledge, patterns and models are being used in intelligent navigational services, including navigational decision support applications, smart tourist guides and navigational suggestion making apps. |
| first_indexed | 2025-11-14T19:24:55Z |
| format | Article |
| id | nottingham-35034 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T19:24:55Z |
| publishDate | 2015 |
| publisher | Rinton Press |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-350342020-05-04T17:17:00Z https://eprints.nottingham.ac.uk/35034/ Automatic detection of points of interest using spatio-temporal data mining Basiri, Anahid Marsh, Stuart Moore, Terry Amiran, Pouria Location Based Services (LBS) are still in their infancy but they are evolving rapidly. It is expected to have more intelligent, adaptive and predictive LBS applications in the future, which can detect users’ intentions and understand their needs, demands and responses. To have such intelligent services, LBS applications should be able to understand users’ behaviours, preferences and interests automatically and without needing users to be asked to specify them. Then, using users’ current situations and previously extracted behaviours, interests and preferences, LBS applications could provide the most appropriate sets of services. This paper shows the application of data mining techniques over anonymous sets of tracking data to recognise mobility behaviours and extract some navigational user preferences such as Point of Interests (PoI) in a format of if-then rules, spatial patterns, models and knowledge. Such knowledge, patterns and models are being used in intelligent navigational services, including navigational decision support applications, smart tourist guides and navigational suggestion making apps. Rinton Press 2015-09-15 Article PeerReviewed Basiri, Anahid, Marsh, Stuart, Moore, Terry and Amiran, Pouria (2015) Automatic detection of points of interest using spatio-temporal data mining. Journal of Mobile Multimedia, 11 (3&4). pp. 193-204. ISSN 1550-4646 http://www.rintonpress.com/xjmm11/jmm-11-34/193-204.pdf |
| spellingShingle | Basiri, Anahid Marsh, Stuart Moore, Terry Amiran, Pouria Automatic detection of points of interest using spatio-temporal data mining |
| title | Automatic detection of points of interest using spatio-temporal data mining |
| title_full | Automatic detection of points of interest using spatio-temporal data mining |
| title_fullStr | Automatic detection of points of interest using spatio-temporal data mining |
| title_full_unstemmed | Automatic detection of points of interest using spatio-temporal data mining |
| title_short | Automatic detection of points of interest using spatio-temporal data mining |
| title_sort | automatic detection of points of interest using spatio-temporal data mining |
| url | https://eprints.nottingham.ac.uk/35034/ https://eprints.nottingham.ac.uk/35034/ |