Efficient mining of extraordinary patterns by pruning and predicting

Pattern mining is an important data mining technology. The existing pattern mining algorithms mainly focus on discovery of ordinary patterns in databases, for example, frequent pattern mining finds patterns with high frequencies and utility pattern mining discovers patterns with high utilities. Howe...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Liu, Junqiang [verfasserIn] Chang, Zhongmin [verfasserIn] Leung, Carson K.S. [verfasserIn] Wong, Raymond C.W. [verfasserIn] Xu, Yabo [verfasserIn] Zhao, Rong [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2019

Schlagwörter:	Data mining Pattern mining Frequent patterns High utility patterns Extraordinary patterns

Übergeordnetes Werk:	Enthalten in: Expert systems with applications - Amsterdam [u.a.] : Elsevier Science, 1990, 125, Seite 55-68
Übergeordnetes Werk:	volume:125 ; pages:55-68

DOI / URN:	10.1016/j.eswa.2019.01.079

Katalog-ID:	ELV001887092

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520			\|a Pattern mining is an important data mining technology. The existing pattern mining algorithms mainly focus on discovery of ordinary patterns in databases, for example, frequent pattern mining finds patterns with high frequencies and utility pattern mining discovers patterns with high utilities. However, in many real-world applications, people are more interested in finding extraordinary patterns with low frequencies and high utilities or with high frequencies and low utilities. While mining ordinary patterns is computationally hard, it is even harder to mine extraordinary patterns. In particular, a two-phase approach that first generates and materializes candidates (high-frequency patterns or high-utility patterns) and then finds extraordinary patterns from the candidates, suffers from the scalability and efficiency bottlenecks.
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650		4	\|a Extraordinary patterns
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700	1		\|a Zhao, Rong \|e verfasserin \|4 aut
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allfields	10.1016/j.eswa.2019.01.079 doi (DE-627)ELV001887092 (ELSEVIER)S0957-4174(19)30095-8 DE-627 ger DE-627 rda eng 004 DE-600 54.72 bkl Liu, Junqiang verfasserin aut Efficient mining of extraordinary patterns by pruning and predicting 2019 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Pattern mining is an important data mining technology. The existing pattern mining algorithms mainly focus on discovery of ordinary patterns in databases, for example, frequent pattern mining finds patterns with high frequencies and utility pattern mining discovers patterns with high utilities. However, in many real-world applications, people are more interested in finding extraordinary patterns with low frequencies and high utilities or with high frequencies and low utilities. While mining ordinary patterns is computationally hard, it is even harder to mine extraordinary patterns. In particular, a two-phase approach that first generates and materializes candidates (high-frequency patterns or high-utility patterns) and then finds extraordinary patterns from the candidates, suffers from the scalability and efficiency bottlenecks. Data mining Pattern mining Frequent patterns High utility patterns Extraordinary patterns Chang, Zhongmin verfasserin aut Leung, Carson K.S. verfasserin (orcid)0000-0002-7541-9127 aut Wong, Raymond C.W. verfasserin aut Xu, Yabo verfasserin aut Zhao, Rong verfasserin aut Enthalten in Expert systems with applications Amsterdam [u.a.] : Elsevier Science, 1990 125, Seite 55-68 Online-Ressource (DE-627)320577961 (DE-600)2017237-0 (DE-576)11481807X nnns volume:125 pages:55-68 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 54.72 Künstliche Intelligenz AR 125 55-68
spelling	10.1016/j.eswa.2019.01.079 doi (DE-627)ELV001887092 (ELSEVIER)S0957-4174(19)30095-8 DE-627 ger DE-627 rda eng 004 DE-600 54.72 bkl Liu, Junqiang verfasserin aut Efficient mining of extraordinary patterns by pruning and predicting 2019 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Pattern mining is an important data mining technology. The existing pattern mining algorithms mainly focus on discovery of ordinary patterns in databases, for example, frequent pattern mining finds patterns with high frequencies and utility pattern mining discovers patterns with high utilities. However, in many real-world applications, people are more interested in finding extraordinary patterns with low frequencies and high utilities or with high frequencies and low utilities. While mining ordinary patterns is computationally hard, it is even harder to mine extraordinary patterns. In particular, a two-phase approach that first generates and materializes candidates (high-frequency patterns or high-utility patterns) and then finds extraordinary patterns from the candidates, suffers from the scalability and efficiency bottlenecks. Data mining Pattern mining Frequent patterns High utility patterns Extraordinary patterns Chang, Zhongmin verfasserin aut Leung, Carson K.S. verfasserin (orcid)0000-0002-7541-9127 aut Wong, Raymond C.W. verfasserin aut Xu, Yabo verfasserin aut Zhao, Rong verfasserin aut Enthalten in Expert systems with applications Amsterdam [u.a.] : Elsevier Science, 1990 125, Seite 55-68 Online-Ressource (DE-627)320577961 (DE-600)2017237-0 (DE-576)11481807X nnns volume:125 pages:55-68 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 54.72 Künstliche Intelligenz AR 125 55-68
allfields_unstemmed	10.1016/j.eswa.2019.01.079 doi (DE-627)ELV001887092 (ELSEVIER)S0957-4174(19)30095-8 DE-627 ger DE-627 rda eng 004 DE-600 54.72 bkl Liu, Junqiang verfasserin aut Efficient mining of extraordinary patterns by pruning and predicting 2019 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Pattern mining is an important data mining technology. The existing pattern mining algorithms mainly focus on discovery of ordinary patterns in databases, for example, frequent pattern mining finds patterns with high frequencies and utility pattern mining discovers patterns with high utilities. However, in many real-world applications, people are more interested in finding extraordinary patterns with low frequencies and high utilities or with high frequencies and low utilities. While mining ordinary patterns is computationally hard, it is even harder to mine extraordinary patterns. In particular, a two-phase approach that first generates and materializes candidates (high-frequency patterns or high-utility patterns) and then finds extraordinary patterns from the candidates, suffers from the scalability and efficiency bottlenecks. Data mining Pattern mining Frequent patterns High utility patterns Extraordinary patterns Chang, Zhongmin verfasserin aut Leung, Carson K.S. verfasserin (orcid)0000-0002-7541-9127 aut Wong, Raymond C.W. verfasserin aut Xu, Yabo verfasserin aut Zhao, Rong verfasserin aut Enthalten in Expert systems with applications Amsterdam [u.a.] : Elsevier Science, 1990 125, Seite 55-68 Online-Ressource (DE-627)320577961 (DE-600)2017237-0 (DE-576)11481807X nnns volume:125 pages:55-68 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 54.72 Künstliche Intelligenz AR 125 55-68
allfieldsGer	10.1016/j.eswa.2019.01.079 doi (DE-627)ELV001887092 (ELSEVIER)S0957-4174(19)30095-8 DE-627 ger DE-627 rda eng 004 DE-600 54.72 bkl Liu, Junqiang verfasserin aut Efficient mining of extraordinary patterns by pruning and predicting 2019 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Pattern mining is an important data mining technology. The existing pattern mining algorithms mainly focus on discovery of ordinary patterns in databases, for example, frequent pattern mining finds patterns with high frequencies and utility pattern mining discovers patterns with high utilities. However, in many real-world applications, people are more interested in finding extraordinary patterns with low frequencies and high utilities or with high frequencies and low utilities. While mining ordinary patterns is computationally hard, it is even harder to mine extraordinary patterns. In particular, a two-phase approach that first generates and materializes candidates (high-frequency patterns or high-utility patterns) and then finds extraordinary patterns from the candidates, suffers from the scalability and efficiency bottlenecks. Data mining Pattern mining Frequent patterns High utility patterns Extraordinary patterns Chang, Zhongmin verfasserin aut Leung, Carson K.S. verfasserin (orcid)0000-0002-7541-9127 aut Wong, Raymond C.W. verfasserin aut Xu, Yabo verfasserin aut Zhao, Rong verfasserin aut Enthalten in Expert systems with applications Amsterdam [u.a.] : Elsevier Science, 1990 125, Seite 55-68 Online-Ressource (DE-627)320577961 (DE-600)2017237-0 (DE-576)11481807X nnns volume:125 pages:55-68 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 54.72 Künstliche Intelligenz AR 125 55-68
allfieldsSound	10.1016/j.eswa.2019.01.079 doi (DE-627)ELV001887092 (ELSEVIER)S0957-4174(19)30095-8 DE-627 ger DE-627 rda eng 004 DE-600 54.72 bkl Liu, Junqiang verfasserin aut Efficient mining of extraordinary patterns by pruning and predicting 2019 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Pattern mining is an important data mining technology. The existing pattern mining algorithms mainly focus on discovery of ordinary patterns in databases, for example, frequent pattern mining finds patterns with high frequencies and utility pattern mining discovers patterns with high utilities. However, in many real-world applications, people are more interested in finding extraordinary patterns with low frequencies and high utilities or with high frequencies and low utilities. While mining ordinary patterns is computationally hard, it is even harder to mine extraordinary patterns. In particular, a two-phase approach that first generates and materializes candidates (high-frequency patterns or high-utility patterns) and then finds extraordinary patterns from the candidates, suffers from the scalability and efficiency bottlenecks. Data mining Pattern mining Frequent patterns High utility patterns Extraordinary patterns Chang, Zhongmin verfasserin aut Leung, Carson K.S. verfasserin (orcid)0000-0002-7541-9127 aut Wong, Raymond C.W. verfasserin aut Xu, Yabo verfasserin aut Zhao, Rong verfasserin aut Enthalten in Expert systems with applications Amsterdam [u.a.] : Elsevier Science, 1990 125, Seite 55-68 Online-Ressource (DE-627)320577961 (DE-600)2017237-0 (DE-576)11481807X nnns volume:125 pages:55-68 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 54.72 Künstliche Intelligenz AR 125 55-68
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authorswithroles_txt_mv	Liu, Junqiang @@aut@@ Chang, Zhongmin @@aut@@ Leung, Carson K.S. @@aut@@ Wong, Raymond C.W. @@aut@@ Xu, Yabo @@aut@@ Zhao, Rong @@aut@@
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author	Liu, Junqiang
spellingShingle	Liu, Junqiang ddc 004 bkl 54.72 misc Data mining misc Pattern mining misc Frequent patterns misc High utility patterns misc Extraordinary patterns Efficient mining of extraordinary patterns by pruning and predicting
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topic_title	004 DE-600 54.72 bkl Efficient mining of extraordinary patterns by pruning and predicting Data mining Pattern mining Frequent patterns High utility patterns Extraordinary patterns
topic	ddc 004 bkl 54.72 misc Data mining misc Pattern mining misc Frequent patterns misc High utility patterns misc Extraordinary patterns
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title	Efficient mining of extraordinary patterns by pruning and predicting
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title_full	Efficient mining of extraordinary patterns by pruning and predicting
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title_sort	efficient mining of extraordinary patterns by pruning and predicting
title_auth	Efficient mining of extraordinary patterns by pruning and predicting
abstract	Pattern mining is an important data mining technology. The existing pattern mining algorithms mainly focus on discovery of ordinary patterns in databases, for example, frequent pattern mining finds patterns with high frequencies and utility pattern mining discovers patterns with high utilities. However, in many real-world applications, people are more interested in finding extraordinary patterns with low frequencies and high utilities or with high frequencies and low utilities. While mining ordinary patterns is computationally hard, it is even harder to mine extraordinary patterns. In particular, a two-phase approach that first generates and materializes candidates (high-frequency patterns or high-utility patterns) and then finds extraordinary patterns from the candidates, suffers from the scalability and efficiency bottlenecks.
abstractGer	Pattern mining is an important data mining technology. The existing pattern mining algorithms mainly focus on discovery of ordinary patterns in databases, for example, frequent pattern mining finds patterns with high frequencies and utility pattern mining discovers patterns with high utilities. However, in many real-world applications, people are more interested in finding extraordinary patterns with low frequencies and high utilities or with high frequencies and low utilities. While mining ordinary patterns is computationally hard, it is even harder to mine extraordinary patterns. In particular, a two-phase approach that first generates and materializes candidates (high-frequency patterns or high-utility patterns) and then finds extraordinary patterns from the candidates, suffers from the scalability and efficiency bottlenecks.
abstract_unstemmed	Pattern mining is an important data mining technology. The existing pattern mining algorithms mainly focus on discovery of ordinary patterns in databases, for example, frequent pattern mining finds patterns with high frequencies and utility pattern mining discovers patterns with high utilities. However, in many real-world applications, people are more interested in finding extraordinary patterns with low frequencies and high utilities or with high frequencies and low utilities. While mining ordinary patterns is computationally hard, it is even harder to mine extraordinary patterns. In particular, a two-phase approach that first generates and materializes candidates (high-frequency patterns or high-utility patterns) and then finds extraordinary patterns from the candidates, suffers from the scalability and efficiency bottlenecks.
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title_short	Efficient mining of extraordinary patterns by pruning and predicting
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author2	Chang, Zhongmin Leung, Carson K.S. Wong, Raymond C.W. Xu, Yabo Zhao, Rong
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doi_str	10.1016/j.eswa.2019.01.079
up_date	2024-07-06T22:54:10.283Z
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The existing pattern mining algorithms mainly focus on discovery of ordinary patterns in databases, for example, frequent pattern mining finds patterns with high frequencies and utility pattern mining discovers patterns with high utilities. However, in many real-world applications, people are more interested in finding extraordinary patterns with low frequencies and high utilities or with high frequencies and low utilities. While mining ordinary patterns is computationally hard, it is even harder to mine extraordinary patterns. In particular, a two-phase approach that first generates and materializes candidates (high-frequency patterns or high-utility patterns) and then finds extraordinary patterns from the candidates, suffers from the scalability and efficiency bottlenecks.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Data mining</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Pattern mining</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Frequent patterns</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">High utility patterns</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Extraordinary patterns</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Chang, Zhongmin</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Leung, 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