Local support-based partition algorithm for frequent pattern mining
Abstract Frequent pattern (itemset) mining is one of the established approaches for knowledge discovery. Minimizing the number of database scans (I/O overhead) is a challenging task in Frequent itemset mining. Partition algorithm is one of the early novel approaches to reduce the database I/O overhe...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Kadappa, Vijayakumar [verfasserIn] Nagesh, Shivaraju [verfasserIn] |
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E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2018 |
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Enthalten in: Pattern Analysis & Applications - Springer-Verlag, 1999, 22(2018), 3 vom: 05. Okt., Seite 1137-1147 |
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volume:22 ; year:2018 ; number:3 ; day:05 ; month:10 ; pages:1137-1147 |
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10.1007/s10044-018-0752-x |
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| 520 | |a Abstract Frequent pattern (itemset) mining is one of the established approaches for knowledge discovery. Minimizing the number of database scans (I/O overhead) is a challenging task in Frequent itemset mining. Partition algorithm is one of the early novel approaches to reduce the database I/O overhead as compared to Apriori algorithm and other related methods. However, Partition algorithm suffers from a significant database I/O overhead (that is, it reads the database twice from the secondary storage) and higher time complexity for computation of frequent itemsets in large databases. In this work, an improved partition algorithm is proposed, which reads the database only once and makes use of local support information to avoid further scans of the database. The proposed algorithm outperforms Apriori and Partition algorithms and shows closer performance to FP-Growth algorithm, in terms of computational time. The proposed method outpaces FP-Growth algorithm in terms of memory usage and is competitive to other algorithms. In terms of database access time, the proposed method exhibits better performance over FP-Growth, Partition and Apriori methods. | ||
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10.1007/s10044-018-0752-x doi (DE-627)SPR008218617 (SPR)s10044-018-0752-x-e DE-627 ger DE-627 rakwb eng Kadappa, Vijayakumar verfasserin aut Local support-based partition algorithm for frequent pattern mining 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Frequent pattern (itemset) mining is one of the established approaches for knowledge discovery. Minimizing the number of database scans (I/O overhead) is a challenging task in Frequent itemset mining. Partition algorithm is one of the early novel approaches to reduce the database I/O overhead as compared to Apriori algorithm and other related methods. However, Partition algorithm suffers from a significant database I/O overhead (that is, it reads the database twice from the secondary storage) and higher time complexity for computation of frequent itemsets in large databases. In this work, an improved partition algorithm is proposed, which reads the database only once and makes use of local support information to avoid further scans of the database. The proposed algorithm outperforms Apriori and Partition algorithms and shows closer performance to FP-Growth algorithm, in terms of computational time. The proposed method outpaces FP-Growth algorithm in terms of memory usage and is competitive to other algorithms. In terms of database access time, the proposed method exhibits better performance over FP-Growth, Partition and Apriori methods. Data mining (dpeaa)DE-He213 Frequent itemset mining (dpeaa)DE-He213 Partition algorithm (dpeaa)DE-He213 Nagesh, Shivaraju verfasserin aut Enthalten in Pattern Analysis & Applications Springer-Verlag, 1999 22(2018), 3 vom: 05. Okt., Seite 1137-1147 (DE-627)SPR008209189 nnns volume:22 year:2018 number:3 day:05 month:10 pages:1137-1147 https://dx.doi.org/10.1007/s10044-018-0752-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 22 2018 3 05 10 1137-1147 |
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10.1007/s10044-018-0752-x doi (DE-627)SPR008218617 (SPR)s10044-018-0752-x-e DE-627 ger DE-627 rakwb eng Kadappa, Vijayakumar verfasserin aut Local support-based partition algorithm for frequent pattern mining 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Frequent pattern (itemset) mining is one of the established approaches for knowledge discovery. Minimizing the number of database scans (I/O overhead) is a challenging task in Frequent itemset mining. Partition algorithm is one of the early novel approaches to reduce the database I/O overhead as compared to Apriori algorithm and other related methods. However, Partition algorithm suffers from a significant database I/O overhead (that is, it reads the database twice from the secondary storage) and higher time complexity for computation of frequent itemsets in large databases. In this work, an improved partition algorithm is proposed, which reads the database only once and makes use of local support information to avoid further scans of the database. The proposed algorithm outperforms Apriori and Partition algorithms and shows closer performance to FP-Growth algorithm, in terms of computational time. The proposed method outpaces FP-Growth algorithm in terms of memory usage and is competitive to other algorithms. In terms of database access time, the proposed method exhibits better performance over FP-Growth, Partition and Apriori methods. Data mining (dpeaa)DE-He213 Frequent itemset mining (dpeaa)DE-He213 Partition algorithm (dpeaa)DE-He213 Nagesh, Shivaraju verfasserin aut Enthalten in Pattern Analysis & Applications Springer-Verlag, 1999 22(2018), 3 vom: 05. Okt., Seite 1137-1147 (DE-627)SPR008209189 nnns volume:22 year:2018 number:3 day:05 month:10 pages:1137-1147 https://dx.doi.org/10.1007/s10044-018-0752-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 22 2018 3 05 10 1137-1147 |
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10.1007/s10044-018-0752-x doi (DE-627)SPR008218617 (SPR)s10044-018-0752-x-e DE-627 ger DE-627 rakwb eng Kadappa, Vijayakumar verfasserin aut Local support-based partition algorithm for frequent pattern mining 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Frequent pattern (itemset) mining is one of the established approaches for knowledge discovery. Minimizing the number of database scans (I/O overhead) is a challenging task in Frequent itemset mining. Partition algorithm is one of the early novel approaches to reduce the database I/O overhead as compared to Apriori algorithm and other related methods. However, Partition algorithm suffers from a significant database I/O overhead (that is, it reads the database twice from the secondary storage) and higher time complexity for computation of frequent itemsets in large databases. In this work, an improved partition algorithm is proposed, which reads the database only once and makes use of local support information to avoid further scans of the database. The proposed algorithm outperforms Apriori and Partition algorithms and shows closer performance to FP-Growth algorithm, in terms of computational time. The proposed method outpaces FP-Growth algorithm in terms of memory usage and is competitive to other algorithms. In terms of database access time, the proposed method exhibits better performance over FP-Growth, Partition and Apriori methods. Data mining (dpeaa)DE-He213 Frequent itemset mining (dpeaa)DE-He213 Partition algorithm (dpeaa)DE-He213 Nagesh, Shivaraju verfasserin aut Enthalten in Pattern Analysis & Applications Springer-Verlag, 1999 22(2018), 3 vom: 05. Okt., Seite 1137-1147 (DE-627)SPR008209189 nnns volume:22 year:2018 number:3 day:05 month:10 pages:1137-1147 https://dx.doi.org/10.1007/s10044-018-0752-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 22 2018 3 05 10 1137-1147 |
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Abstract Frequent pattern (itemset) mining is one of the established approaches for knowledge discovery. Minimizing the number of database scans (I/O overhead) is a challenging task in Frequent itemset mining. Partition algorithm is one of the early novel approaches to reduce the database I/O overhead as compared to Apriori algorithm and other related methods. However, Partition algorithm suffers from a significant database I/O overhead (that is, it reads the database twice from the secondary storage) and higher time complexity for computation of frequent itemsets in large databases. In this work, an improved partition algorithm is proposed, which reads the database only once and makes use of local support information to avoid further scans of the database. The proposed algorithm outperforms Apriori and Partition algorithms and shows closer performance to FP-Growth algorithm, in terms of computational time. The proposed method outpaces FP-Growth algorithm in terms of memory usage and is competitive to other algorithms. In terms of database access time, the proposed method exhibits better performance over FP-Growth, Partition and Apriori methods. |
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Abstract Frequent pattern (itemset) mining is one of the established approaches for knowledge discovery. Minimizing the number of database scans (I/O overhead) is a challenging task in Frequent itemset mining. Partition algorithm is one of the early novel approaches to reduce the database I/O overhead as compared to Apriori algorithm and other related methods. However, Partition algorithm suffers from a significant database I/O overhead (that is, it reads the database twice from the secondary storage) and higher time complexity for computation of frequent itemsets in large databases. In this work, an improved partition algorithm is proposed, which reads the database only once and makes use of local support information to avoid further scans of the database. The proposed algorithm outperforms Apriori and Partition algorithms and shows closer performance to FP-Growth algorithm, in terms of computational time. The proposed method outpaces FP-Growth algorithm in terms of memory usage and is competitive to other algorithms. In terms of database access time, the proposed method exhibits better performance over FP-Growth, Partition and Apriori methods. |
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Abstract Frequent pattern (itemset) mining is one of the established approaches for knowledge discovery. Minimizing the number of database scans (I/O overhead) is a challenging task in Frequent itemset mining. Partition algorithm is one of the early novel approaches to reduce the database I/O overhead as compared to Apriori algorithm and other related methods. However, Partition algorithm suffers from a significant database I/O overhead (that is, it reads the database twice from the secondary storage) and higher time complexity for computation of frequent itemsets in large databases. In this work, an improved partition algorithm is proposed, which reads the database only once and makes use of local support information to avoid further scans of the database. The proposed algorithm outperforms Apriori and Partition algorithms and shows closer performance to FP-Growth algorithm, in terms of computational time. The proposed method outpaces FP-Growth algorithm in terms of memory usage and is competitive to other algorithms. In terms of database access time, the proposed method exhibits better performance over FP-Growth, Partition and Apriori methods. |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">SPR008218617</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20201124023812.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">201005s2018 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s10044-018-0752-x</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR008218617</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s10044-018-0752-x-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Kadappa, Vijayakumar</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Local support-based partition algorithm for frequent pattern mining</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2018</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Frequent pattern (itemset) mining is one of the established approaches for knowledge discovery. Minimizing the number of database scans (I/O overhead) is a challenging task in Frequent itemset mining. Partition algorithm is one of the early novel approaches to reduce the database I/O overhead as compared to Apriori algorithm and other related methods. However, Partition algorithm suffers from a significant database I/O overhead (that is, it reads the database twice from the secondary storage) and higher time complexity for computation of frequent itemsets in large databases. In this work, an improved partition algorithm is proposed, which reads the database only once and makes use of local support information to avoid further scans of the database. The proposed algorithm outperforms Apriori and Partition algorithms and shows closer performance to FP-Growth algorithm, in terms of computational time. The proposed method outpaces FP-Growth algorithm in terms of memory usage and is competitive to other algorithms. In terms of database access time, the proposed method exhibits better performance over FP-Growth, Partition and Apriori methods.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Data mining</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Frequent itemset mining</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Partition algorithm</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Nagesh, Shivaraju</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Pattern Analysis & Applications</subfield><subfield code="d">Springer-Verlag, 1999</subfield><subfield code="g">22(2018), 3 vom: 05. Okt., Seite 1137-1147</subfield><subfield code="w">(DE-627)SPR008209189</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:22</subfield><subfield code="g">year:2018</subfield><subfield code="g">number:3</subfield><subfield code="g">day:05</subfield><subfield code="g">month:10</subfield><subfield code="g">pages:1137-1147</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://dx.doi.org/10.1007/s10044-018-0752-x</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_SPRINGER</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">22</subfield><subfield code="j">2018</subfield><subfield code="e">3</subfield><subfield code="b">05</subfield><subfield code="c">10</subfield><subfield code="h">1137-1147</subfield></datafield></record></collection>
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