A New Approach for Mining Order-Preserving Submatrices Based on All Common Subsequences

Order-preserving submatrices (OPSMs) have been applied in many fields, such as DNA microarray data analysis, automatic recommendation systems, and target marketing systems, as an important unsupervised learning model. Unfortunately, most existing methods are heuristic algorithms which are unable to...
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Autor*in:	Yun Xue [verfasserIn] Zhengling Liao [verfasserIn] Meihang Li [verfasserIn] Jie Luo [verfasserIn] Qiuhua Kuang [verfasserIn] Xiaohui Hu [verfasserIn] Tiechen Li [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2015

Übergeordnetes Werk:	In: Computational and Mathematical Methods in Medicine - Hindawi Limited, 2011, (2015)
Übergeordnetes Werk:	year:2015

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc Journal toc

DOI / URN:	10.1155/2015/680434

Katalog-ID:	DOAJ023584181

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spelling	10.1155/2015/680434 doi (DE-627)DOAJ023584181 (DE-599)DOAJf53bb608f3434f119b876c2ce4eb0e6c DE-627 ger DE-627 rakwb eng R858-859.7 Yun Xue verfasserin aut A New Approach for Mining Order-Preserving Submatrices Based on All Common Subsequences 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Order-preserving submatrices (OPSMs) have been applied in many fields, such as DNA microarray data analysis, automatic recommendation systems, and target marketing systems, as an important unsupervised learning model. Unfortunately, most existing methods are heuristic algorithms which are unable to reveal OPSMs entirely in NP-complete problem. In particular, deep OPSMs, corresponding to long patterns with few supporting sequences, incur explosive computational costs and are completely pruned by most popular methods. In this paper, we propose an exact method to discover all OPSMs based on frequent sequential pattern mining. First, an existing algorithm was adjusted to disclose all common subsequence (ACS) between every two row sequences, and therefore all deep OPSMs will not be missed. Then, an improved data structure for prefix tree was used to store and traverse ACS, and Apriori principle was employed to efficiently mine the frequent sequential pattern. Finally, experiments were implemented on gene and synthetic datasets. Results demonstrated the effectiveness and efficiency of this method. Computer applications to medicine. Medical informatics Zhengling Liao verfasserin aut Meihang Li verfasserin aut Jie Luo verfasserin aut Qiuhua Kuang verfasserin aut Xiaohui Hu verfasserin aut Tiechen Li verfasserin aut In Computational and Mathematical Methods in Medicine Hindawi Limited, 2011 (2015) (DE-627)519764781 (DE-600)2256917-0 1748670X nnns year:2015 https://doi.org/10.1155/2015/680434 kostenfrei https://doaj.org/article/f53bb608f3434f119b876c2ce4eb0e6c kostenfrei http://dx.doi.org/10.1155/2015/680434 kostenfrei https://doaj.org/toc/1748-670X Journal toc kostenfrei https://doaj.org/toc/1748-6718 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 2015
allfields_unstemmed	10.1155/2015/680434 doi (DE-627)DOAJ023584181 (DE-599)DOAJf53bb608f3434f119b876c2ce4eb0e6c DE-627 ger DE-627 rakwb eng R858-859.7 Yun Xue verfasserin aut A New Approach for Mining Order-Preserving Submatrices Based on All Common Subsequences 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Order-preserving submatrices (OPSMs) have been applied in many fields, such as DNA microarray data analysis, automatic recommendation systems, and target marketing systems, as an important unsupervised learning model. Unfortunately, most existing methods are heuristic algorithms which are unable to reveal OPSMs entirely in NP-complete problem. In particular, deep OPSMs, corresponding to long patterns with few supporting sequences, incur explosive computational costs and are completely pruned by most popular methods. In this paper, we propose an exact method to discover all OPSMs based on frequent sequential pattern mining. First, an existing algorithm was adjusted to disclose all common subsequence (ACS) between every two row sequences, and therefore all deep OPSMs will not be missed. Then, an improved data structure for prefix tree was used to store and traverse ACS, and Apriori principle was employed to efficiently mine the frequent sequential pattern. Finally, experiments were implemented on gene and synthetic datasets. Results demonstrated the effectiveness and efficiency of this method. Computer applications to medicine. Medical informatics Zhengling Liao verfasserin aut Meihang Li verfasserin aut Jie Luo verfasserin aut Qiuhua Kuang verfasserin aut Xiaohui Hu verfasserin aut Tiechen Li verfasserin aut In Computational and Mathematical Methods in Medicine Hindawi Limited, 2011 (2015) (DE-627)519764781 (DE-600)2256917-0 1748670X nnns year:2015 https://doi.org/10.1155/2015/680434 kostenfrei https://doaj.org/article/f53bb608f3434f119b876c2ce4eb0e6c kostenfrei http://dx.doi.org/10.1155/2015/680434 kostenfrei https://doaj.org/toc/1748-670X Journal toc kostenfrei https://doaj.org/toc/1748-6718 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 2015
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allfieldsSound	10.1155/2015/680434 doi (DE-627)DOAJ023584181 (DE-599)DOAJf53bb608f3434f119b876c2ce4eb0e6c DE-627 ger DE-627 rakwb eng R858-859.7 Yun Xue verfasserin aut A New Approach for Mining Order-Preserving Submatrices Based on All Common Subsequences 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Order-preserving submatrices (OPSMs) have been applied in many fields, such as DNA microarray data analysis, automatic recommendation systems, and target marketing systems, as an important unsupervised learning model. Unfortunately, most existing methods are heuristic algorithms which are unable to reveal OPSMs entirely in NP-complete problem. In particular, deep OPSMs, corresponding to long patterns with few supporting sequences, incur explosive computational costs and are completely pruned by most popular methods. In this paper, we propose an exact method to discover all OPSMs based on frequent sequential pattern mining. First, an existing algorithm was adjusted to disclose all common subsequence (ACS) between every two row sequences, and therefore all deep OPSMs will not be missed. Then, an improved data structure for prefix tree was used to store and traverse ACS, and Apriori principle was employed to efficiently mine the frequent sequential pattern. Finally, experiments were implemented on gene and synthetic datasets. Results demonstrated the effectiveness and efficiency of this method. Computer applications to medicine. Medical informatics Zhengling Liao verfasserin aut Meihang Li verfasserin aut Jie Luo verfasserin aut Qiuhua Kuang verfasserin aut Xiaohui Hu verfasserin aut Tiechen Li verfasserin aut In Computational and Mathematical Methods in Medicine Hindawi Limited, 2011 (2015) (DE-627)519764781 (DE-600)2256917-0 1748670X nnns year:2015 https://doi.org/10.1155/2015/680434 kostenfrei https://doaj.org/article/f53bb608f3434f119b876c2ce4eb0e6c kostenfrei http://dx.doi.org/10.1155/2015/680434 kostenfrei https://doaj.org/toc/1748-670X Journal toc kostenfrei https://doaj.org/toc/1748-6718 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 2015
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topic_title	R858-859.7 A New Approach for Mining Order-Preserving Submatrices Based on All Common Subsequences
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title	A New Approach for Mining Order-Preserving Submatrices Based on All Common Subsequences
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title_full	A New Approach for Mining Order-Preserving Submatrices Based on All Common Subsequences
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title_sort	new approach for mining order-preserving submatrices based on all common subsequences
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title_auth	A New Approach for Mining Order-Preserving Submatrices Based on All Common Subsequences
abstract	Order-preserving submatrices (OPSMs) have been applied in many fields, such as DNA microarray data analysis, automatic recommendation systems, and target marketing systems, as an important unsupervised learning model. Unfortunately, most existing methods are heuristic algorithms which are unable to reveal OPSMs entirely in NP-complete problem. In particular, deep OPSMs, corresponding to long patterns with few supporting sequences, incur explosive computational costs and are completely pruned by most popular methods. In this paper, we propose an exact method to discover all OPSMs based on frequent sequential pattern mining. First, an existing algorithm was adjusted to disclose all common subsequence (ACS) between every two row sequences, and therefore all deep OPSMs will not be missed. Then, an improved data structure for prefix tree was used to store and traverse ACS, and Apriori principle was employed to efficiently mine the frequent sequential pattern. Finally, experiments were implemented on gene and synthetic datasets. Results demonstrated the effectiveness and efficiency of this method.
abstractGer	Order-preserving submatrices (OPSMs) have been applied in many fields, such as DNA microarray data analysis, automatic recommendation systems, and target marketing systems, as an important unsupervised learning model. Unfortunately, most existing methods are heuristic algorithms which are unable to reveal OPSMs entirely in NP-complete problem. In particular, deep OPSMs, corresponding to long patterns with few supporting sequences, incur explosive computational costs and are completely pruned by most popular methods. In this paper, we propose an exact method to discover all OPSMs based on frequent sequential pattern mining. First, an existing algorithm was adjusted to disclose all common subsequence (ACS) between every two row sequences, and therefore all deep OPSMs will not be missed. Then, an improved data structure for prefix tree was used to store and traverse ACS, and Apriori principle was employed to efficiently mine the frequent sequential pattern. Finally, experiments were implemented on gene and synthetic datasets. Results demonstrated the effectiveness and efficiency of this method.
abstract_unstemmed	Order-preserving submatrices (OPSMs) have been applied in many fields, such as DNA microarray data analysis, automatic recommendation systems, and target marketing systems, as an important unsupervised learning model. Unfortunately, most existing methods are heuristic algorithms which are unable to reveal OPSMs entirely in NP-complete problem. In particular, deep OPSMs, corresponding to long patterns with few supporting sequences, incur explosive computational costs and are completely pruned by most popular methods. In this paper, we propose an exact method to discover all OPSMs based on frequent sequential pattern mining. First, an existing algorithm was adjusted to disclose all common subsequence (ACS) between every two row sequences, and therefore all deep OPSMs will not be missed. Then, an improved data structure for prefix tree was used to store and traverse ACS, and Apriori principle was employed to efficiently mine the frequent sequential pattern. Finally, experiments were implemented on gene and synthetic datasets. Results demonstrated the effectiveness and efficiency of this method.
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title_short	A New Approach for Mining Order-Preserving Submatrices Based on All Common Subsequences
url	https://doi.org/10.1155/2015/680434 https://doaj.org/article/f53bb608f3434f119b876c2ce4eb0e6c http://dx.doi.org/10.1155/2015/680434 https://doaj.org/toc/1748-670X https://doaj.org/toc/1748-6718
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up_date	2024-07-03T18:24:59.629Z
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