Exploiting Multiple Radii to Learn Significant Locations
Location contexts are important for many context-aware applications. A significant location is a specialized form of location context for expressing a user’s daily activity. We propose a method to cluster positions measured by cellular phones into significant locations with multiple radii. Cellular...
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| creator | Toyama, Norio Ota, Takashi Kato, Fumihiro Toyota, Youichi Hattori, Takashi Hagino, Tatsuya |
| description | Location contexts are important for many context-aware applications. A significant location is a specialized form of location context for expressing a user’s daily activity. We propose a method to cluster positions measured by cellular phones into significant locations with multiple radii. Cellular phones we used are equipped with a positioning system, where data can be taken in low frequency with wide-varying estimated errors. In order to learn significant locations, our system exploits multiple radii for coping with these characteristics and for adapting to a variety of users’ spatial behavioral patterns. We also discuss appropriate parameters for our clustering method. |
| doi_str_mv | 10.1007/11426646_15 |
| format | Conference Proceeding |
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A significant location is a specialized form of location context for expressing a user’s daily activity. We propose a method to cluster positions measured by cellular phones into significant locations with multiple radii. Cellular phones we used are equipped with a positioning system, where data can be taken in low frequency with wide-varying estimated errors. In order to learn significant locations, our system exploits multiple radii for coping with these characteristics and for adapting to a variety of users’ spatial behavioral patterns. We also discuss appropriate parameters for our clustering method.</description><subject>Applied sciences</subject><subject>Cellular Phone</subject><subject>Computer science; control theory; systems</subject><subject>Computer systems and distributed systems. User interface</subject><subject>Exact sciences and technology</subject><subject>Location Candidate</subject><subject>Software</subject><subject>Spatial Behavior</subject><subject>Threshold Density</subject><subject>Ubiquitous Environment</subject><issn>0302-9743</issn><issn>1611-3349</issn><isbn>9783540258964</isbn><isbn>3540258965</isbn><isbn>3540320423</isbn><isbn>9783540320425</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2005</creationdate><recordtype>conference_proceeding</recordtype><recordid>eNpNkDlPxDAUhM0lsSxb8QfSUFAEnq8Xu0RoOaQgJI7ash1nZQhOFAcJ_j1ZLQXTjDTfaIoh5IzCJQWorigVDFGgoXKPnHApgDMQjO-TBUVKS86FPiArXaktY1JpFIdkARxYqSvBj8kq53eYxalCVS2IWn8PXR-nmDbF41c3xaELxbNtYiymvqiDHVPxEjcpttHbNBV17-0U-5RPyVFruxxWf74kb7fr15v7sn66e7i5rkvPkE4l1w4heKc9tlhJaEBaBxikn1PlWKWFdRJQiCbMSKHQSjYyNNYFULLlS3K-2x1s9rZrR5t8zGYY46cdfwxFzXiFMPcudr08o7QJo3F9_5ENBbO9zvy7jv8CMC1aeA</recordid><startdate>2005</startdate><enddate>2005</enddate><creator>Toyama, Norio</creator><creator>Ota, Takashi</creator><creator>Kato, Fumihiro</creator><creator>Toyota, Youichi</creator><creator>Hattori, Takashi</creator><creator>Hagino, Tatsuya</creator><general>Springer Berlin Heidelberg</general><general>Springer</general><scope>IQODW</scope></search><sort><creationdate>2005</creationdate><title>Exploiting Multiple Radii to Learn Significant Locations</title><author>Toyama, Norio ; Ota, Takashi ; Kato, Fumihiro ; Toyota, Youichi ; Hattori, Takashi ; Hagino, Tatsuya</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c261t-39b60ecb9c6f6750d05ab06e5c0ec8b2794ab50644de05a864985d5edabe085f3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2005</creationdate><topic>Applied sciences</topic><topic>Cellular Phone</topic><topic>Computer science; control theory; systems</topic><topic>Computer systems and distributed systems. 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A significant location is a specialized form of location context for expressing a user’s daily activity. We propose a method to cluster positions measured by cellular phones into significant locations with multiple radii. Cellular phones we used are equipped with a positioning system, where data can be taken in low frequency with wide-varying estimated errors. In order to learn significant locations, our system exploits multiple radii for coping with these characteristics and for adapting to a variety of users’ spatial behavioral patterns. We also discuss appropriate parameters for our clustering method.</abstract><cop>Berlin, Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><doi>10.1007/11426646_15</doi><tpages>12</tpages></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0302-9743 |
| ispartof | Lecture notes in computer science, 2005, p.157-168 |
| issn | 0302-9743 1611-3349 |
| language | eng |
| recordid | cdi_pascalfrancis_primary_16923760 |
| source | Springer Books |
| subjects | Applied sciences Cellular Phone Computer science control theory systems Computer systems and distributed systems. User interface Exact sciences and technology Location Candidate Software Spatial Behavior Threshold Density Ubiquitous Environment |
| title | Exploiting Multiple Radii to Learn Significant Locations |
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