Rare association rule mining from incremental databases
Abstract Rare association rule mining is an imperative field of data mining that attempts to identify rare correlations among the items in a database. Although numerous attempts pertaining to rare association rule mining can be found in the literature, there are still certain issues that need utmost...
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Gespeichert in:
| Autor*in: |
Borah, Anindita [verfasserIn] Nath, Bhabesh [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2018 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Pattern Analysis & Applications - Springer-Verlag, 1999, 23(2018), 1 vom: 10. Nov., Seite 113-134 |
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| Übergeordnetes Werk: |
volume:23 ; year:2018 ; number:1 ; day:10 ; month:11 ; pages:113-134 |
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| DOI / URN: |
10.1007/s10044-018-0759-3 |
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| 520 | |a Abstract Rare association rule mining is an imperative field of data mining that attempts to identify rare correlations among the items in a database. Although numerous attempts pertaining to rare association rule mining can be found in the literature, there are still certain issues that need utmost attention. The most prominent one among them is the rare association rule mining from incremental databases. The existing rare association rule mining techniques are capable of operating only on static databases, assuming that the entire database to be operated on is available during the outset of the mining process. Inclusion of new records, however, may lead to the generation of some new interesting rules from the current set of data, invalidating the previously extracted significant rare association rules. Executing the entire mining process from scratch for the newly arrived set of data could be a tedious affair. With a view to resolve the issue of incremental rare association rule mining, this study presents a single-pass tree-based approach for extracting rare association rules when new data are inserted into the original database. The proposed approach is capable of generating the complete set of frequent and rare patterns without rescanning the updated database and reconstructing the entire tree structure when new transactions are added to the existent database. Experimental evaluation has been carried out on several benchmark real and synthetic datasets to analyze the efficiency of the proposed approach. Furthermore, to assess its applicability in real-world applications, experimental analysis has been performed on a real geological dataset where earthquake records are incrementally being added on an annual basis. Comparative performance analysis demonstrates the preeminence of proposed approach over existing frequent and rare association rule mining techniques. | ||
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10.1007/s10044-018-0759-3 doi (DE-627)SPR008219044 (SPR)s10044-018-0759-3-e DE-627 ger DE-627 rakwb eng Borah, Anindita verfasserin aut Rare association rule mining from incremental databases 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Rare association rule mining is an imperative field of data mining that attempts to identify rare correlations among the items in a database. Although numerous attempts pertaining to rare association rule mining can be found in the literature, there are still certain issues that need utmost attention. The most prominent one among them is the rare association rule mining from incremental databases. The existing rare association rule mining techniques are capable of operating only on static databases, assuming that the entire database to be operated on is available during the outset of the mining process. Inclusion of new records, however, may lead to the generation of some new interesting rules from the current set of data, invalidating the previously extracted significant rare association rules. Executing the entire mining process from scratch for the newly arrived set of data could be a tedious affair. With a view to resolve the issue of incremental rare association rule mining, this study presents a single-pass tree-based approach for extracting rare association rules when new data are inserted into the original database. The proposed approach is capable of generating the complete set of frequent and rare patterns without rescanning the updated database and reconstructing the entire tree structure when new transactions are added to the existent database. Experimental evaluation has been carried out on several benchmark real and synthetic datasets to analyze the efficiency of the proposed approach. Furthermore, to assess its applicability in real-world applications, experimental analysis has been performed on a real geological dataset where earthquake records are incrementally being added on an annual basis. Comparative performance analysis demonstrates the preeminence of proposed approach over existing frequent and rare association rule mining techniques. Rare pattern (dpeaa)DE-He213 Association rule (dpeaa)DE-He213 Rare association rule (dpeaa)DE-He213 Incremental mining (dpeaa)DE-He213 Nath, Bhabesh verfasserin aut Enthalten in Pattern Analysis & Applications Springer-Verlag, 1999 23(2018), 1 vom: 10. Nov., Seite 113-134 (DE-627)SPR008209189 nnns volume:23 year:2018 number:1 day:10 month:11 pages:113-134 https://dx.doi.org/10.1007/s10044-018-0759-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 23 2018 1 10 11 113-134 |
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10.1007/s10044-018-0759-3 doi (DE-627)SPR008219044 (SPR)s10044-018-0759-3-e DE-627 ger DE-627 rakwb eng Borah, Anindita verfasserin aut Rare association rule mining from incremental databases 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Rare association rule mining is an imperative field of data mining that attempts to identify rare correlations among the items in a database. Although numerous attempts pertaining to rare association rule mining can be found in the literature, there are still certain issues that need utmost attention. The most prominent one among them is the rare association rule mining from incremental databases. The existing rare association rule mining techniques are capable of operating only on static databases, assuming that the entire database to be operated on is available during the outset of the mining process. Inclusion of new records, however, may lead to the generation of some new interesting rules from the current set of data, invalidating the previously extracted significant rare association rules. Executing the entire mining process from scratch for the newly arrived set of data could be a tedious affair. With a view to resolve the issue of incremental rare association rule mining, this study presents a single-pass tree-based approach for extracting rare association rules when new data are inserted into the original database. The proposed approach is capable of generating the complete set of frequent and rare patterns without rescanning the updated database and reconstructing the entire tree structure when new transactions are added to the existent database. Experimental evaluation has been carried out on several benchmark real and synthetic datasets to analyze the efficiency of the proposed approach. Furthermore, to assess its applicability in real-world applications, experimental analysis has been performed on a real geological dataset where earthquake records are incrementally being added on an annual basis. Comparative performance analysis demonstrates the preeminence of proposed approach over existing frequent and rare association rule mining techniques. Rare pattern (dpeaa)DE-He213 Association rule (dpeaa)DE-He213 Rare association rule (dpeaa)DE-He213 Incremental mining (dpeaa)DE-He213 Nath, Bhabesh verfasserin aut Enthalten in Pattern Analysis & Applications Springer-Verlag, 1999 23(2018), 1 vom: 10. Nov., Seite 113-134 (DE-627)SPR008209189 nnns volume:23 year:2018 number:1 day:10 month:11 pages:113-134 https://dx.doi.org/10.1007/s10044-018-0759-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 23 2018 1 10 11 113-134 |
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10.1007/s10044-018-0759-3 doi (DE-627)SPR008219044 (SPR)s10044-018-0759-3-e DE-627 ger DE-627 rakwb eng Borah, Anindita verfasserin aut Rare association rule mining from incremental databases 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Rare association rule mining is an imperative field of data mining that attempts to identify rare correlations among the items in a database. Although numerous attempts pertaining to rare association rule mining can be found in the literature, there are still certain issues that need utmost attention. The most prominent one among them is the rare association rule mining from incremental databases. The existing rare association rule mining techniques are capable of operating only on static databases, assuming that the entire database to be operated on is available during the outset of the mining process. Inclusion of new records, however, may lead to the generation of some new interesting rules from the current set of data, invalidating the previously extracted significant rare association rules. Executing the entire mining process from scratch for the newly arrived set of data could be a tedious affair. With a view to resolve the issue of incremental rare association rule mining, this study presents a single-pass tree-based approach for extracting rare association rules when new data are inserted into the original database. The proposed approach is capable of generating the complete set of frequent and rare patterns without rescanning the updated database and reconstructing the entire tree structure when new transactions are added to the existent database. Experimental evaluation has been carried out on several benchmark real and synthetic datasets to analyze the efficiency of the proposed approach. Furthermore, to assess its applicability in real-world applications, experimental analysis has been performed on a real geological dataset where earthquake records are incrementally being added on an annual basis. Comparative performance analysis demonstrates the preeminence of proposed approach over existing frequent and rare association rule mining techniques. Rare pattern (dpeaa)DE-He213 Association rule (dpeaa)DE-He213 Rare association rule (dpeaa)DE-He213 Incremental mining (dpeaa)DE-He213 Nath, Bhabesh verfasserin aut Enthalten in Pattern Analysis & Applications Springer-Verlag, 1999 23(2018), 1 vom: 10. Nov., Seite 113-134 (DE-627)SPR008209189 nnns volume:23 year:2018 number:1 day:10 month:11 pages:113-134 https://dx.doi.org/10.1007/s10044-018-0759-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 23 2018 1 10 11 113-134 |
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Abstract Rare association rule mining is an imperative field of data mining that attempts to identify rare correlations among the items in a database. Although numerous attempts pertaining to rare association rule mining can be found in the literature, there are still certain issues that need utmost attention. The most prominent one among them is the rare association rule mining from incremental databases. The existing rare association rule mining techniques are capable of operating only on static databases, assuming that the entire database to be operated on is available during the outset of the mining process. Inclusion of new records, however, may lead to the generation of some new interesting rules from the current set of data, invalidating the previously extracted significant rare association rules. Executing the entire mining process from scratch for the newly arrived set of data could be a tedious affair. With a view to resolve the issue of incremental rare association rule mining, this study presents a single-pass tree-based approach for extracting rare association rules when new data are inserted into the original database. The proposed approach is capable of generating the complete set of frequent and rare patterns without rescanning the updated database and reconstructing the entire tree structure when new transactions are added to the existent database. Experimental evaluation has been carried out on several benchmark real and synthetic datasets to analyze the efficiency of the proposed approach. Furthermore, to assess its applicability in real-world applications, experimental analysis has been performed on a real geological dataset where earthquake records are incrementally being added on an annual basis. Comparative performance analysis demonstrates the preeminence of proposed approach over existing frequent and rare association rule mining techniques. |
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Abstract Rare association rule mining is an imperative field of data mining that attempts to identify rare correlations among the items in a database. Although numerous attempts pertaining to rare association rule mining can be found in the literature, there are still certain issues that need utmost attention. The most prominent one among them is the rare association rule mining from incremental databases. The existing rare association rule mining techniques are capable of operating only on static databases, assuming that the entire database to be operated on is available during the outset of the mining process. Inclusion of new records, however, may lead to the generation of some new interesting rules from the current set of data, invalidating the previously extracted significant rare association rules. Executing the entire mining process from scratch for the newly arrived set of data could be a tedious affair. With a view to resolve the issue of incremental rare association rule mining, this study presents a single-pass tree-based approach for extracting rare association rules when new data are inserted into the original database. The proposed approach is capable of generating the complete set of frequent and rare patterns without rescanning the updated database and reconstructing the entire tree structure when new transactions are added to the existent database. Experimental evaluation has been carried out on several benchmark real and synthetic datasets to analyze the efficiency of the proposed approach. Furthermore, to assess its applicability in real-world applications, experimental analysis has been performed on a real geological dataset where earthquake records are incrementally being added on an annual basis. Comparative performance analysis demonstrates the preeminence of proposed approach over existing frequent and rare association rule mining techniques. |
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Abstract Rare association rule mining is an imperative field of data mining that attempts to identify rare correlations among the items in a database. Although numerous attempts pertaining to rare association rule mining can be found in the literature, there are still certain issues that need utmost attention. The most prominent one among them is the rare association rule mining from incremental databases. The existing rare association rule mining techniques are capable of operating only on static databases, assuming that the entire database to be operated on is available during the outset of the mining process. Inclusion of new records, however, may lead to the generation of some new interesting rules from the current set of data, invalidating the previously extracted significant rare association rules. Executing the entire mining process from scratch for the newly arrived set of data could be a tedious affair. With a view to resolve the issue of incremental rare association rule mining, this study presents a single-pass tree-based approach for extracting rare association rules when new data are inserted into the original database. The proposed approach is capable of generating the complete set of frequent and rare patterns without rescanning the updated database and reconstructing the entire tree structure when new transactions are added to the existent database. Experimental evaluation has been carried out on several benchmark real and synthetic datasets to analyze the efficiency of the proposed approach. Furthermore, to assess its applicability in real-world applications, experimental analysis has been performed on a real geological dataset where earthquake records are incrementally being added on an annual basis. Comparative performance analysis demonstrates the preeminence of proposed approach over existing frequent and rare association rule mining techniques. |
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Rare association rule mining from incremental databases |
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Nath, Bhabesh |
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10.1007/s10044-018-0759-3 |
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