Discovering multi-time-interval sequential patterns in sequence database
碩士 === 國立中央大學 === 資訊管理研究所 === 92 === Sequential pattern mining is of great importance in many applications including computational biology study, consumer behavior analysis, system performance analysis, etc. Recently, an extension of sequential patterns, called time-interval sequential patterns, is...
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ndltd-TW-092NCU053960902016-06-08T04:13:36Z http://ndltd.ncl.edu.tw/handle/01355867510245382572 Discovering multi-time-interval sequential patterns in sequence database 在序列資料庫中挖掘多重時間間隔樣式 Hui-Ru Yang 楊慧如 碩士 國立中央大學 資訊管理研究所 92 Sequential pattern mining is of great importance in many applications including computational biology study, consumer behavior analysis, system performance analysis, etc. Recently, an extension of sequential patterns, called time-interval sequential patterns, is proposed by Chen, Jiang, and Ko, which not only reveals the order of items but also the time intervals between successive items. For example: having bought a laser printer, a customer returns to buy a scanner in three months and then a CD burner in six months. Although time-interval sequential patterns are useful in predicting when the customer would take the next step, it can not determine when the next k steps will be taken. Hence, we present two efficient algorithms, MI-Apriori and MI-PrefixSpan to solve this problem. The experimental results show that the MI-PrefixSpan algorithm is faster than the MI-Apriori algorithm but the MI-Apriori algorithm has a better scalability. Yen-Liang Chen 陳彥良 2004 學位論文 ; thesis 61 en_US |
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碩士 === 國立中央大學 === 資訊管理研究所 === 92 === Sequential pattern mining is of great importance in many applications including computational biology study, consumer behavior analysis, system performance analysis, etc. Recently, an extension of sequential patterns, called time-interval sequential patterns, is proposed by Chen, Jiang, and Ko, which not only reveals the order of items but also the time intervals between successive items. For example: having bought a laser printer, a customer returns to buy a scanner in three months and then a CD burner in six months. Although time-interval sequential patterns are useful in predicting when the customer would take the next step, it can not determine when the next k steps will be taken. Hence, we present two efficient algorithms, MI-Apriori and MI-PrefixSpan to solve this problem. The experimental results show that the MI-PrefixSpan algorithm is faster than the MI-Apriori algorithm but the MI-Apriori algorithm has a better scalability.
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Yen-Liang Chen |
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Yen-Liang Chen Hui-Ru Yang 楊慧如 |
author |
Hui-Ru Yang 楊慧如 |
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Hui-Ru Yang 楊慧如 Discovering multi-time-interval sequential patterns in sequence database |
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Hui-Ru Yang |
title |
Discovering multi-time-interval sequential patterns in sequence database |
title_short |
Discovering multi-time-interval sequential patterns in sequence database |
title_full |
Discovering multi-time-interval sequential patterns in sequence database |
title_fullStr |
Discovering multi-time-interval sequential patterns in sequence database |
title_full_unstemmed |
Discovering multi-time-interval sequential patterns in sequence database |
title_sort |
discovering multi-time-interval sequential patterns in sequence database |
publishDate |
2004 |
url |
http://ndltd.ncl.edu.tw/handle/01355867510245382572 |
work_keys_str_mv |
AT huiruyang discoveringmultitimeintervalsequentialpatternsinsequencedatabase AT yánghuìrú discoveringmultitimeintervalsequentialpatternsinsequencedatabase AT huiruyang zàixùlièzīliàokùzhōngwājuéduōzhòngshíjiānjiāngéyàngshì AT yánghuìrú zàixùlièzīliàokùzhōngwājuéduōzhòngshíjiānjiāngéyàngshì |
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