關鍵字查詢 | 類別:會議論文 | | 關鍵字:Timed Sequential Pattern Mining Based on Confidence in Accumulated Intervals

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序號 學年期 教師動態
1 103/1 資管系 徐煥智 教授 會議論文 發佈 Timed Sequential Pattern Mining Based on Confidence in Accumulated Intervals , [103-1] :Timed Sequential Pattern Mining Based on Confidence in Accumulated Intervals會議論文Timed Sequential Pattern Mining Based on Confidence in Accumulated IntervalsJou, Chichang; Shyur, Huan-Jyh; Yen, Chih-Yu淡江大學資訊管理學系timed sequential pattern mining; accumulated interval; PrefixSpanIEEEProceedings of the 15th International Conference on Information Reuse and IntegrationIEEEMany applications of sequential patterns require a guarantee of a particular event happening within a period of time. We propose CAI-PrefixSpan, a new data mining algorithm to obtain confident timed sequential patterns from sequential databases. Based on PrefixSpan, it takes advantage of the pattern-growth approach. After a particular event sequence, it would first calculate the confidence level regarding the eventual occurrence of a particular event. For those pass the minimal confidence requirement, it then computes the minimal time interval that satisfies the support requirement. It then generates corresponding projected
2 103/1 資管系 周清江 副教授 會議論文 發佈 Timed Sequential Pattern Mining Based on Confidence in Accumulated Intervals , [103-1] :Timed Sequential Pattern Mining Based on Confidence in Accumulated Intervals會議論文Timed Sequential Pattern Mining Based on Confidence in Accumulated IntervalsJou, Chichang; Shyur, Huan-Jyh; Yen, Chih-Yu淡江大學資訊管理學系timed sequential pattern mining; accumulated interval; PrefixSpanIEEEProceedings of the 15th International Conference on Information Reuse and IntegrationIEEEMany applications of sequential patterns require a guarantee of a particular event happening within a period of time. We propose CAI-PrefixSpan, a new data mining algorithm to obtain confident timed sequential patterns from sequential databases. Based on PrefixSpan, it takes advantage of the pattern-growth approach. After a particular event sequence, it would first calculate the confidence level regarding the eventual occurrence of a particular event. For those pass the minimal confidence requirement, it then computes the minimal time interval that satisfies the support requirement. It then generates corresponding projected
3 103/1 資管系 徐煥智 教授 會議論文 發佈 Timed Sequential Pattern Mining Based on Confidence in Accumulated Intervals , [103-1] :Timed Sequential Pattern Mining Based on Confidence in Accumulated Intervals會議論文Timed Sequential Pattern Mining Based on Confidence in Accumulated IntervalsJou, Chichang; Shyur, Huan-Jyh; Yen, Chih-Yu淡江大學資訊管理學系15th IEEE International Conference on Information Reuse and IntegrationIEEEtku_id: ; 000110620;Submitted by 煥智 徐 (110620@mail.tku.edu.tw) on 2014-10-24 No. of bitstreams: 0;Made available in DSpace on 2014-10-24 (GMT). No. of bitstreams: 0;2014-10-24 補正完成 by 何雯婷en_US國際20140813~20140815YUSA15th IEEE International Conference on Information Reuse and IntegrationSan Francisco, USA<links><record><name>機構典藏連結</name><url>http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/99302</url></record></links>
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