Mining Hierarchical Time‐interval Sequential Patterns in Sequence Databases
碩士 === 國立中正大學 === 資訊管理所暨醫療資訊管理所 === 98 === Mining sequential patterns is an important issue in data mining and has many applications. An extended work of sequential pattern mining, called hierarchical time-interval sequential pattern mining, is proposed to retrieve time-interval information between...
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ndltd-TW-098CCU057770412015-10-13T18:25:32Z http://ndltd.ncl.edu.tw/handle/49035383302851164192 Mining Hierarchical Time‐interval Sequential Patterns in Sequence Databases 在序列資料庫中探勘樹狀階層時間間隔序列模式 Chieh-I Yang 楊杰翊 碩士 國立中正大學 資訊管理所暨醫療資訊管理所 98 Mining sequential patterns is an important issue in data mining and has many applications. An extended work of sequential pattern mining, called hierarchical time-interval sequential pattern mining, is proposed to retrieve time-interval information between successive items. However, previous work only considers single-level time-interval in pattern extraction, which means sequential patterns with cross-level time-intervals are completely ignored. Therefore, this study first defines hierarchical time-interval sequential patterns and then presents a novel algorithm, named HTI-PrefixSpan, for discovering the complete set of hierarchical time-interval sequential patterns. Experimental results show that the proposed algorithm is not only effective on the test dataset but also could discover patterns with cross level time-interval. Fan Wu 吳帆 2010/08/ 學位論文 ; thesis 40 en_US |
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碩士 === 國立中正大學 === 資訊管理所暨醫療資訊管理所 === 98 === Mining sequential patterns is an important issue in data mining and has many
applications. An extended work of sequential pattern mining, called hierarchical
time-interval sequential pattern mining, is proposed to retrieve time-interval
information between successive items. However, previous work only considers
single-level time-interval in pattern extraction, which means sequential patterns with
cross-level time-intervals are completely ignored. Therefore, this study first defines
hierarchical time-interval sequential patterns and then presents a novel algorithm,
named HTI-PrefixSpan, for discovering the complete set of hierarchical time-interval
sequential patterns. Experimental results show that the proposed algorithm is not only
effective on the test dataset but also could discover patterns with cross level
time-interval.
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Fan Wu |
author_facet |
Fan Wu Chieh-I Yang 楊杰翊 |
author |
Chieh-I Yang 楊杰翊 |
spellingShingle |
Chieh-I Yang 楊杰翊 Mining Hierarchical Time‐interval Sequential Patterns in Sequence Databases |
author_sort |
Chieh-I Yang |
title |
Mining Hierarchical Time‐interval Sequential Patterns in Sequence Databases |
title_short |
Mining Hierarchical Time‐interval Sequential Patterns in Sequence Databases |
title_full |
Mining Hierarchical Time‐interval Sequential Patterns in Sequence Databases |
title_fullStr |
Mining Hierarchical Time‐interval Sequential Patterns in Sequence Databases |
title_full_unstemmed |
Mining Hierarchical Time‐interval Sequential Patterns in Sequence Databases |
title_sort |
mining hierarchical time‐interval sequential patterns in sequence databases |
publishDate |
2010 |
url |
http://ndltd.ncl.edu.tw/handle/49035383302851164192 |
work_keys_str_mv |
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