Parallel and distributed association rule mining in life science: A novel parallel algorithm to mine genomics data
•A parallel algorithm for Association rule mining.•Association rule mining of genomics data.•A dynamic workload balancing algorithm for FP-Growth. Association rule mining (ARM) is largely employed in several scientific areas and application domains, and many different algorithms for learning associa...
Uložené v:
| Vydané v: | Information sciences Ročník 575; s. 747 - 761 |
|---|---|
| Hlavní autori: | , , |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
Elsevier Inc
01.10.2021
|
| Predmet: | |
| ISSN: | 0020-0255, 1872-6291 |
| On-line prístup: | Získať plný text |
| Tagy: |
Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
|
| Abstract | •A parallel algorithm for Association rule mining.•Association rule mining of genomics data.•A dynamic workload balancing algorithm for FP-Growth.
Association rule mining (ARM) is largely employed in several scientific areas and application domains, and many different algorithms for learning association rules from databases have been introduced. Despite the presence of many existing algorithms, there is still room for the introduction of novel approaches tailored for novel kinds of datasets. Because often the efficiency of such algorithms depends on the type of analyzed dataset. For instance, classical ARM algorithms present some drawbacks for biological datasets produced by microarray technologies in particular containing Single Nucleotide Polymorphisms (SNPs). In particular classical algorithms require large execution times also with small datasets. Therefore the possibility to improve the performance of such algorithms by leveraging parallel computing is a growing research area. The main contributions of this paper are: a comparison among different sequential, parallels and distributed ARM techniques, and the presentation of a novel ARM algorithm, named Balanced Parallel Association Rule Extractor from SNPs (BPARES), that employs parallel computing and a novel balancing strategy to improve response time. BPARES improves performance without loosing in accuracy as well as it handles more efficiently the available computational power and reduces the memory consumption. |
|---|---|
| AbstractList | •A parallel algorithm for Association rule mining.•Association rule mining of genomics data.•A dynamic workload balancing algorithm for FP-Growth.
Association rule mining (ARM) is largely employed in several scientific areas and application domains, and many different algorithms for learning association rules from databases have been introduced. Despite the presence of many existing algorithms, there is still room for the introduction of novel approaches tailored for novel kinds of datasets. Because often the efficiency of such algorithms depends on the type of analyzed dataset. For instance, classical ARM algorithms present some drawbacks for biological datasets produced by microarray technologies in particular containing Single Nucleotide Polymorphisms (SNPs). In particular classical algorithms require large execution times also with small datasets. Therefore the possibility to improve the performance of such algorithms by leveraging parallel computing is a growing research area. The main contributions of this paper are: a comparison among different sequential, parallels and distributed ARM techniques, and the presentation of a novel ARM algorithm, named Balanced Parallel Association Rule Extractor from SNPs (BPARES), that employs parallel computing and a novel balancing strategy to improve response time. BPARES improves performance without loosing in accuracy as well as it handles more efficiently the available computational power and reduces the memory consumption. |
| Author | Cannataro, Mario Guzzi, Pietro Hiram Agapito, Giuseppe |
| Author_xml | – sequence: 1 givenname: Giuseppe surname: Agapito fullname: Agapito, Giuseppe email: agapito@unicz.it – sequence: 2 givenname: Pietro Hiram surname: Guzzi fullname: Guzzi, Pietro Hiram email: hguzzi@unicz.it – sequence: 3 givenname: Mario orcidid: 0000-0003-1502-2387 surname: Cannataro fullname: Cannataro, Mario email: cannataro@unicz.it |
| BookMark | eNp9kMtKAzEYRoNUsK0-gLu8wIx_0s5NV6V4g4IudB0ySab-JZOUJC349k5tceGiq291PjhnQkbOO0PILYOcASvvNjm6mHNgdQ5VDkVxQcasrnhW8oaNyBiAQwa8KK7IJMYNAMyrshyT8C6DtNZYKp2mGmMK2O6S0VTG6BXKhN7RsLOG9ujQrSk6arEzNCo0Tpl7uqDO74eD7d-TXfuA6aunyR8oQ9fG-R5VpFomeU0uO2mjuTntlHw-PX4sX7LV2_PrcrHKFG-qlHXNICY7UFB2XDctL5tZpdVct5oXNZuzrq7KRrdct6ZmkpdMg9QMFGtmEkDPpqQ6_qrgYwymEwrTr08KEq1gIA7pxEYM6cQhnYBKDOkGkv0jtwF7Gb7PMg9HxgxKezRBnAJpDEYloT2eoX8AWXSLOw |
| CitedBy_id | crossref_primary_10_1016_j_ins_2022_10_110 crossref_primary_10_1016_j_imu_2023_101432 crossref_primary_10_1186_s12859_022_04936_z crossref_primary_10_3389_fphar_2025_1548991 crossref_primary_10_1007_s41060_023_00456_y crossref_primary_10_1007_s10115_024_02105_7 crossref_primary_10_21511_kpm_05_1__2021_01 crossref_primary_10_3390_math12243930 |
| Cites_doi | 10.1186/1471-2105-13-258 10.1007/s10115-015-0884-x 10.1109/TC.2013.176 10.1109/69.846291 10.1016/j.jbi.2015.06.005 10.1007/s13174-010-0007-6 10.1016/j.bbrc.2014.01.151 10.1007/978-3-662-07952-2_2 10.1145/1327452.1327492 10.1007/978-3-642-21916-0 10.1093/nar/gkv1115 10.1007/978-3-540-24596-4_20 10.1145/568271.223813 10.1016/j.eswa.2014.01.025 10.1109/69.553164 10.1007/s00280-015-2916-3 10.18632/oncotarget.4302 10.1093/bioinformatics/btp595 |
| ContentType | Journal Article |
| Copyright | 2018 Elsevier Inc. |
| Copyright_xml | – notice: 2018 Elsevier Inc. |
| DBID | AAYXX CITATION |
| DOI | 10.1016/j.ins.2018.07.055 |
| DatabaseName | CrossRef |
| DatabaseTitle | CrossRef |
| DatabaseTitleList | |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering Library & Information Science |
| EISSN | 1872-6291 |
| EndPage | 761 |
| ExternalDocumentID | 10_1016_j_ins_2018_07_055 S0020025518305723 |
| GroupedDBID | --K --M --Z -~X .DC .~1 0R~ 1B1 1OL 1RT 1~. 1~5 29I 4.4 457 4G. 5GY 5VS 7-5 71M 8P~ 9JN 9JO AAAKF AAAKG AABNK AACTN AAEDT AAEDW AAIAV AAIKJ AAKOC AALRI AAOAW AAQFI AAQXK AARIN AAXUO AAYFN ABAOU ABBOA ABEFU ABFNM ABJNI ABMAC ABTAH ABUCO ABXDB ABYKQ ACAZW ACDAQ ACGFS ACNNM ACRLP ACZNC ADBBV ADEZE ADGUI ADJOM ADMUD ADTZH AEBSH AECPX AEKER AENEX AFFNX AFKWA AFTJW AGHFR AGUBO AGYEJ AHHHB AHJVU AHZHX AIALX AIEXJ AIGVJ AIKHN AITUG AJBFU AJOXV ALMA_UNASSIGNED_HOLDINGS AMFUW AMRAJ AOUOD APLSM ARUGR ASPBG AVWKF AXJTR AZFZN BJAXD BKOJK BLXMC CS3 DU5 EBS EFJIC EFLBG EJD EO8 EO9 EP2 EP3 F5P FDB FEDTE FGOYB FIRID FNPLU FYGXN G-Q GBLVA GBOLZ HAMUX HLZ HVGLF HZ~ H~9 IHE J1W JJJVA KOM LG9 LY1 M41 MHUIS MO0 MS~ N9A O-L O9- OAUVE OZT P-8 P-9 P2P PC. Q38 R2- RIG ROL RPZ SBC SDF SDG SDP SDS SES SEW SPC SPCBC SSB SSD SST SSV SSW SSZ T5K TN5 TWZ UHS WH7 WUQ XPP YYP ZMT ZY4 ~02 ~G- 77I 9DU AATTM AAXKI AAYWO AAYXX ABWVN ACLOT ACRPL ACVFH ADCNI ADNMO ADVLN AEIPS AEUPX AFJKZ AFPUW AGQPQ AIGII AIIUN AKBMS AKRWK AKYEP ANKPU APXCP CITATION EFKBS ~HD |
| ID | FETCH-LOGICAL-c297t-f9101af0c06f2d9b26937dc4dbd258141f8769db2dbe81a261d0ad10c193a00d3 |
| ISICitedReferencesCount | 12 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000696947900020&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 0020-0255 |
| IngestDate | Tue Nov 18 22:42:15 EST 2025 Sat Nov 29 07:26:48 EST 2025 Fri Feb 23 02:44:37 EST 2024 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Keywords | Parallel data mining Association rules mining Multi-threading Genomics data |
| Language | English |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-c297t-f9101af0c06f2d9b26937dc4dbd258141f8769db2dbe81a261d0ad10c193a00d3 |
| ORCID | 0000-0003-1502-2387 |
| PageCount | 15 |
| ParticipantIDs | crossref_citationtrail_10_1016_j_ins_2018_07_055 crossref_primary_10_1016_j_ins_2018_07_055 elsevier_sciencedirect_doi_10_1016_j_ins_2018_07_055 |
| PublicationCentury | 2000 |
| PublicationDate | October 2021 2021-10-00 |
| PublicationDateYYYYMMDD | 2021-10-01 |
| PublicationDate_xml | – month: 10 year: 2021 text: October 2021 |
| PublicationDecade | 2020 |
| PublicationTitle | Information sciences |
| PublicationYear | 2021 |
| Publisher | Elsevier Inc |
| Publisher_xml | – name: Elsevier Inc |
| References | Guzzi, Agapito, Di Martino, Arbitrio, Tassone, Tagliaferri, Cannataro (bib0006) 2012; 13 Pastrello, Pasini, Kotlyar, Otasek, Wong, Sangrar, Rahmati, Jurisica (bib0010) 2014; 445 Brown, Otasek, Ali, McGuffin, Xie, Devani, Toch, Jurisica (bib0007) 2009; 25 Di Martino, Guzzi, Caracciolo, Agnelli, Neri, Walker, Morgan, Cannataro, Tassone, Tagliaferri (bib0011) 2015; 6 Chen, Gao, Li (bib0034) 2009 Dean, Ghemawat (bib0033) 2008; 51 Zaki (bib0023) 2000; 12 M. Cannataro, A. Congiusta, C. Mastroianni, A. Pugliese, D. Talia, P. Trunfio, Grid-Based Data Mining and Knowledge Discovery, Springer Berlin Heidelberg, Berlin, Heidelberg, pp. 19–45. doi Agapito, Guzzi, Cannataro (bib0016) 2017 Park, Chen, Yu (bib0022) 1995; 24 Deng, Lv (bib0027) 2014; 41 Agrawal, Mannila, Srikant, Toivonen, Verkamo (bib0020) 1996; 12 Savasere, Omiecinski, Navathe (bib0025) 1995 Guzzi, Agapito, Martino, Arbitrio, Tassone, Tagliaferri, Cannataro (bib0028) 2014 A. Veloso, M.E. Otey, S. Parthasarathy, W. Meira, Parallel and Distributed Frequent Itemset Mining on Dynamic Datasets, Springer Berlin Heidelberg, Berlin, Heidelberg, pp. 184–193. doi Li, Wang, Zhang, Zhang, Chang (bib0032) 2008 Kotlyar, Pastrello, Sheahan, Jurisica (bib0009) 2016; 44 Cannataro, Talia (bib0014) 2003 Agrawal, Imieliński, Swami (bib0002) 1993; 22 Guzzi, Agapito, Cannataro (bib0005) 2014; 63 Zhang, Cheng, Boutaba (bib0019) 2010; 1 . Mell, Grance (bib0018) 2011 Agrawal, Imieliński, Swami (bib0001) 1993 Agapito, Milano, Guzzi, Cannataro (bib0029) 2014 Han, Pei, Yin (bib0003) 2000; 29 Han, Pei, Yin (bib0004) 2000 Agapito, Guzzi, Cannataro (bib0015) 2015; 56 Arbitrio, Di Martino, Barbieri, Agapito, Guzzi, Botta, Iuliano, Scionti, Altomare, Codispoti, Conforti, Cannataro, Tassone, Tagliaferri (bib0012) 2016; 77 Agrawal, Shafer (bib0030) 1996; 8 Agapito, Cannataro, Guzzi, Marozzo, Talia, Trunfio (bib0008) 2013 Flynn (bib0017) 2011 Fumarola, Lanotte, Ceci, Malerba (bib0026) 2016; 48 Agarwal, Srikant (bib0021) 1994 M. Kryszkiewicz, H. Rybinski, A. Skowron, Z.W. Raś (Eds.), FAST sequence mining based on sparse Id-lists, Foundations of Intelligent Systems, Springer Berlin Heidelberg, Berlin, Heidelberg, 2011. doi Agrawal (10.1016/j.ins.2018.07.055_bib0030) 1996; 8 10.1016/j.ins.2018.07.055_bib0013 Guzzi (10.1016/j.ins.2018.07.055_bib0005) 2014; 63 Pastrello (10.1016/j.ins.2018.07.055_sbref0010) 2014; 445 Agrawal (10.1016/j.ins.2018.07.055_bib0002) 1993; 22 Kotlyar (10.1016/j.ins.2018.07.055_bib0009) 2016; 44 Guzzi (10.1016/j.ins.2018.07.055_bib0028) 2014 Zaki (10.1016/j.ins.2018.07.055_bib0023) 2000; 12 Zhang (10.1016/j.ins.2018.07.055_bib0019) 2010; 1 Arbitrio (10.1016/j.ins.2018.07.055_bib0012) 2016; 77 Fumarola (10.1016/j.ins.2018.07.055_bib0026) 2016; 48 Agapito (10.1016/j.ins.2018.07.055_bib0008) 2013 Park (10.1016/j.ins.2018.07.055_bib0022) 1995; 24 Agapito (10.1016/j.ins.2018.07.055_bib0015) 2015; 56 10.1016/j.ins.2018.07.055_bib0031 Dean (10.1016/j.ins.2018.07.055_bib0033) 2008; 51 Agapito (10.1016/j.ins.2018.07.055_bib0016) 2017 10.1016/j.ins.2018.07.055_bib0024 Guzzi (10.1016/j.ins.2018.07.055_bib0006) 2012; 13 Savasere (10.1016/j.ins.2018.07.055_bib0025) 1995 Di Martino (10.1016/j.ins.2018.07.055_bib0011) 2015; 6 Deng (10.1016/j.ins.2018.07.055_bib0027) 2014; 41 Chen (10.1016/j.ins.2018.07.055_bib0034) 2009 Agrawal (10.1016/j.ins.2018.07.055_bib0020) 1996; 12 Agrawal (10.1016/j.ins.2018.07.055_bib0001) 1993 Agarwal (10.1016/j.ins.2018.07.055_bib0021) 1994 Cannataro (10.1016/j.ins.2018.07.055_bib0014) 2003 Li (10.1016/j.ins.2018.07.055_bib0032) 2008 Flynn (10.1016/j.ins.2018.07.055_bib0017) 2011 Mell (10.1016/j.ins.2018.07.055_sbref0017) 2011 Han (10.1016/j.ins.2018.07.055_bib0004) 2000 Han (10.1016/j.ins.2018.07.055_bib0003) 2000; 29 Brown (10.1016/j.ins.2018.07.055_bib0007) 2009; 25 Agapito (10.1016/j.ins.2018.07.055_bib0029) 2014 |
| References_xml | – start-page: 468:468 year: 2013 end-page: 468:475 ident: bib0008 article-title: Cloud4SNP: distributed analysis of SNP microarray data on the cloud publication-title: Proceedings of the International Conference on Bioinformatics, Computational Biology and Biomedical Informatics – start-page: 1 year: 2000 end-page: 12 ident: bib0004 article-title: Mining frequent patterns without candidate generation publication-title: Proceedings of the ACM SIGMOD International Conference on Management of Data – start-page: 437 year: 2003 end-page: 441 ident: bib0014 article-title: Towards the next-generation grid: a pervasive environment for knowledge-based computing publication-title: Proceedings of the International Conference on Information Technology: Coding and Computing – volume: 6 start-page: 19132 year: 2015 ident: bib0011 article-title: Integrated analysis of micrornas, transcription factors and target genes expression discloses a specific molecular architecture of hyperdiploid multiple myeloma publication-title: Oncotarget – volume: 445 start-page: 757 year: 2014 end-page: 773 ident: bib0010 article-title: Integration, visualization and analysis of human interactome publication-title: Biochem. Biophys. Res. Commun. – year: 2017 ident: bib0016 article-title: Parallel extraction of association rules from genomics data publication-title: Appl. Math. Comput. – year: 2011 ident: bib0018 publication-title: The NIST Definition of Cloud Computing – volume: 12 start-page: 372 year: 2000 end-page: 390 ident: bib0023 article-title: Scalable algorithms for association mining publication-title: IEEE Trans. Knowl. Data Eng. – volume: 51 start-page: 107 year: 2008 end-page: 113 ident: bib0033 article-title: MapReduce: simplified data processing on large clusters publication-title: Commun. ACM – volume: 1 start-page: 7 year: 2010 end-page: 18 ident: bib0019 article-title: Cloud computing: state-of-the-art and research challenges publication-title: J. Internet Serv. Appl. – reference: M. Cannataro, A. Congiusta, C. Mastroianni, A. Pugliese, D. Talia, P. Trunfio, Grid-Based Data Mining and Knowledge Discovery, Springer Berlin Heidelberg, Berlin, Heidelberg, pp. 19–45. doi: – start-page: 107 year: 2008 end-page: 114 ident: bib0032 article-title: Pfp: parallel fp-growth for query recommendation publication-title: Proceedings of the ACM Conference on Recommender Systems – start-page: 487 year: 1994 end-page: 499 ident: bib0021 article-title: Fast algorithms for mining association rules publication-title: Proceedings of the Twentieth VLDB Conference – start-page: 1 year: 2014 end-page: 8 ident: bib0029 article-title: Improving annotation quality in gene ontology by mining cross-ontology weighted association rules publication-title: Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine (BIBM) – start-page: 432 year: 1995 end-page: 444 ident: bib0025 article-title: An efficient algorithm for mining association rules in large databases publication-title: Proceedings of the Twenty First International Conference on Very Large Data Bases – volume: 12 start-page: 307 year: 1996 end-page: 328 ident: bib0020 article-title: Fast discovery of association rules. publication-title: Adv. Knowl. Discov. Data Min. – volume: 41 start-page: 4505 year: 2014 end-page: 4512 ident: bib0027 article-title: Fast mining frequent itemsets using nodesets publication-title: Expert Syst. Appl. – start-page: 207 year: 1993 end-page: 216 ident: bib0001 article-title: Mining association rules between sets of items in large databases publication-title: Proceedings of the ACM SIGMOD International Conference on Management of Data – volume: 24 start-page: 175 year: 1995 end-page: 186 ident: bib0022 article-title: An effective hash-based algorithm for mining association rules publication-title: Proceedings of the ACM SIGMOD International Conference on Management of Data – volume: 22 start-page: 207 year: 1993 end-page: 216 ident: bib0002 article-title: Mining association rules between sets of items in large databases – volume: 44 start-page: D536 year: 2016 end-page: D541 ident: bib0009 article-title: Integrated interactions database: tissue-specific view of the human and model organism interactomes publication-title: Nucleic Acids Res. – volume: 63 start-page: 2961 year: 2014 end-page: 2974 ident: bib0005 article-title: CoreSNP: parallel processing of microarray data publication-title: IEEE Trans. Comput. – reference: M. Kryszkiewicz, H. Rybinski, A. Skowron, Z.W. Raś (Eds.), FAST sequence mining based on sparse Id-lists, Foundations of Intelligent Systems, Springer Berlin Heidelberg, Berlin, Heidelberg, 2011. doi: – reference: . – volume: 29 start-page: 1 year: 2000 end-page: 12 ident: bib0003 article-title: Mining frequent patterns without candidate generation – start-page: 59 year: 2014 end-page: 62 ident: bib0028 article-title: Dmet-miner: efficient learning of association rules from genotyping data for personalized medicine publication-title: Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine (BIBM) – start-page: 689 year: 2011 end-page: 697 ident: bib0017 article-title: Flynn’s taxonomy publication-title: Encyclopedia of Parallel Computing – volume: 25 start-page: 3327 year: 2009 end-page: 3329 ident: bib0007 article-title: Navigator: network analysis, visualization and graphing toronto publication-title: Bioinformatics – volume: 77 start-page: 205 year: 2016 end-page: 209 ident: bib0012 article-title: Identification of polymorphic variants associated with erlotinib-related skin toxicity in advanced non-small cell lung cancer patients by dmet microarray analysis publication-title: Cancer Chemother. Pharmacol. – volume: 8 start-page: 962 year: 1996 end-page: 969 ident: bib0030 article-title: Parallel mining of association rules publication-title: IEEE Trans. Knowl. Data Eng. – start-page: 283 year: 2009 end-page: 286 ident: bib0034 article-title: An efficient parallel fp-growth algorithm publication-title: Proceedings of the International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery – volume: 56 start-page: 273 year: 2015 end-page: 283 ident: bib0015 article-title: DMET-miner: efficient discovery of association rules from pharmacogenomic data publication-title: J. Biomed. Inf. – volume: 13 start-page: 258 year: 2012 ident: bib0006 article-title: DMET-analyzer: automatic analysis of affymetrix DMET data publication-title: BMC Bioinform. – reference: A. Veloso, M.E. Otey, S. Parthasarathy, W. Meira, Parallel and Distributed Frequent Itemset Mining on Dynamic Datasets, Springer Berlin Heidelberg, Berlin, Heidelberg, pp. 184–193. doi: – volume: 48 start-page: 429 year: 2016 end-page: 463 ident: bib0026 article-title: CloFAST: closed sequential pattern mining using sparse and vertical id-lists publication-title: Knowl. Inf. Syst. – volume: 13 start-page: 258 issue: 1 year: 2012 ident: 10.1016/j.ins.2018.07.055_bib0006 article-title: DMET-analyzer: automatic analysis of affymetrix DMET data publication-title: BMC Bioinform. doi: 10.1186/1471-2105-13-258 – start-page: 432 year: 1995 ident: 10.1016/j.ins.2018.07.055_bib0025 article-title: An efficient algorithm for mining association rules in large databases – volume: 48 start-page: 429 issue: 2 year: 2016 ident: 10.1016/j.ins.2018.07.055_bib0026 article-title: CloFAST: closed sequential pattern mining using sparse and vertical id-lists publication-title: Knowl. Inf. Syst. doi: 10.1007/s10115-015-0884-x – volume: 63 start-page: 2961 issue: 12 year: 2014 ident: 10.1016/j.ins.2018.07.055_bib0005 article-title: CoreSNP: parallel processing of microarray data publication-title: IEEE Trans. Comput. doi: 10.1109/TC.2013.176 – volume: 12 start-page: 372 issue: 3 year: 2000 ident: 10.1016/j.ins.2018.07.055_bib0023 article-title: Scalable algorithms for association mining publication-title: IEEE Trans. Knowl. Data Eng. doi: 10.1109/69.846291 – year: 2011 ident: 10.1016/j.ins.2018.07.055_sbref0017 – volume: 12 start-page: 307 issue: 1 year: 1996 ident: 10.1016/j.ins.2018.07.055_bib0020 article-title: Fast discovery of association rules. publication-title: Adv. Knowl. Discov. Data Min. – volume: 56 start-page: 273 year: 2015 ident: 10.1016/j.ins.2018.07.055_bib0015 article-title: DMET-miner: efficient discovery of association rules from pharmacogenomic data publication-title: J. Biomed. Inf. doi: 10.1016/j.jbi.2015.06.005 – year: 2017 ident: 10.1016/j.ins.2018.07.055_bib0016 article-title: Parallel extraction of association rules from genomics data publication-title: Appl. Math. Comput. – start-page: 207 year: 1993 ident: 10.1016/j.ins.2018.07.055_bib0001 article-title: Mining association rules between sets of items in large databases – start-page: 689 year: 2011 ident: 10.1016/j.ins.2018.07.055_bib0017 article-title: Flynn’s taxonomy – volume: 1 start-page: 7 issue: 1 year: 2010 ident: 10.1016/j.ins.2018.07.055_bib0019 article-title: Cloud computing: state-of-the-art and research challenges publication-title: J. Internet Serv. Appl. doi: 10.1007/s13174-010-0007-6 – volume: 22 start-page: 207 issue: 2 year: 1993 ident: 10.1016/j.ins.2018.07.055_bib0002 article-title: Mining association rules between sets of items in large databases – start-page: 1 year: 2014 ident: 10.1016/j.ins.2018.07.055_bib0029 article-title: Improving annotation quality in gene ontology by mining cross-ontology weighted association rules – start-page: 107 year: 2008 ident: 10.1016/j.ins.2018.07.055_bib0032 article-title: Pfp: parallel fp-growth for query recommendation – volume: 445 start-page: 757 issue: 4 year: 2014 ident: 10.1016/j.ins.2018.07.055_sbref0010 article-title: Integration, visualization and analysis of human interactome publication-title: Biochem. Biophys. Res. Commun. doi: 10.1016/j.bbrc.2014.01.151 – start-page: 437 year: 2003 ident: 10.1016/j.ins.2018.07.055_bib0014 article-title: Towards the next-generation grid: a pervasive environment for knowledge-based computing – ident: 10.1016/j.ins.2018.07.055_bib0013 doi: 10.1007/978-3-662-07952-2_2 – volume: 51 start-page: 107 issue: 1 year: 2008 ident: 10.1016/j.ins.2018.07.055_bib0033 article-title: MapReduce: simplified data processing on large clusters publication-title: Commun. ACM doi: 10.1145/1327452.1327492 – start-page: 1 year: 2000 ident: 10.1016/j.ins.2018.07.055_bib0004 article-title: Mining frequent patterns without candidate generation – ident: 10.1016/j.ins.2018.07.055_bib0024 doi: 10.1007/978-3-642-21916-0 – volume: 44 start-page: D536 issue: D1 year: 2016 ident: 10.1016/j.ins.2018.07.055_bib0009 article-title: Integrated interactions database: tissue-specific view of the human and model organism interactomes publication-title: Nucleic Acids Res. doi: 10.1093/nar/gkv1115 – start-page: 468:468 year: 2013 ident: 10.1016/j.ins.2018.07.055_bib0008 article-title: Cloud4SNP: distributed analysis of SNP microarray data on the cloud – ident: 10.1016/j.ins.2018.07.055_bib0031 doi: 10.1007/978-3-540-24596-4_20 – start-page: 283 year: 2009 ident: 10.1016/j.ins.2018.07.055_bib0034 article-title: An efficient parallel fp-growth algorithm – volume: 24 start-page: 175 issue: 2 year: 1995 ident: 10.1016/j.ins.2018.07.055_bib0022 article-title: An effective hash-based algorithm for mining association rules publication-title: Proceedings of the ACM SIGMOD International Conference on Management of Data doi: 10.1145/568271.223813 – volume: 41 start-page: 4505 issue: 10 year: 2014 ident: 10.1016/j.ins.2018.07.055_bib0027 article-title: Fast mining frequent itemsets using nodesets publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2014.01.025 – volume: 8 start-page: 962 issue: 6 year: 1996 ident: 10.1016/j.ins.2018.07.055_bib0030 article-title: Parallel mining of association rules publication-title: IEEE Trans. Knowl. Data Eng. doi: 10.1109/69.553164 – volume: 77 start-page: 205 issue: 1 year: 2016 ident: 10.1016/j.ins.2018.07.055_bib0012 article-title: Identification of polymorphic variants associated with erlotinib-related skin toxicity in advanced non-small cell lung cancer patients by dmet microarray analysis publication-title: Cancer Chemother. Pharmacol. doi: 10.1007/s00280-015-2916-3 – start-page: 487 year: 1994 ident: 10.1016/j.ins.2018.07.055_bib0021 article-title: Fast algorithms for mining association rules – start-page: 59 year: 2014 ident: 10.1016/j.ins.2018.07.055_bib0028 article-title: Dmet-miner: efficient learning of association rules from genotyping data for personalized medicine – volume: 6 start-page: 19132 issue: 22 year: 2015 ident: 10.1016/j.ins.2018.07.055_bib0011 article-title: Integrated analysis of micrornas, transcription factors and target genes expression discloses a specific molecular architecture of hyperdiploid multiple myeloma publication-title: Oncotarget doi: 10.18632/oncotarget.4302 – volume: 29 start-page: 1 issue: 2 year: 2000 ident: 10.1016/j.ins.2018.07.055_bib0003 article-title: Mining frequent patterns without candidate generation – volume: 25 start-page: 3327 issue: 24 year: 2009 ident: 10.1016/j.ins.2018.07.055_bib0007 article-title: Navigator: network analysis, visualization and graphing toronto publication-title: Bioinformatics doi: 10.1093/bioinformatics/btp595 |
| SSID | ssj0004766 |
| Score | 2.4507802 |
| Snippet | •A parallel algorithm for Association rule mining.•Association rule mining of genomics data.•A dynamic workload balancing algorithm for FP-Growth.
Association... |
| SourceID | crossref elsevier |
| SourceType | Enrichment Source Index Database Publisher |
| StartPage | 747 |
| SubjectTerms | Association rules mining Genomics data Multi-threading Parallel data mining |
| Title | Parallel and distributed association rule mining in life science: A novel parallel algorithm to mine genomics data |
| URI | https://dx.doi.org/10.1016/j.ins.2018.07.055 |
| Volume | 575 |
| WOSCitedRecordID | wos000696947900020&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVESC databaseName: Elsevier SD Freedom Collection Journals 2021 customDbUrl: eissn: 1872-6291 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0004766 issn: 0020-0255 databaseCode: AIEXJ dateStart: 19950101 isFulltext: true titleUrlDefault: https://www.sciencedirect.com providerName: Elsevier |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1Lb9QwELaWlgMcUCmgFmjlA-JAFMlxk3XCbYVKCxLVHoq0t8iJnZIqL6XZVdW_wJ9mHNtJ1AeiBy5RZK3H2cwXz4zt-QahDxHJaCgUGR4PueungXQhsg1dERE5nxNO_YT3xSbY2Vm4WkXL2ey3zYXZFKyqwuvrqPmvqoY2ULZKnX2Eugeh0AD3oHS4gtrh-k-KX_JW1UfRFABC8eKqklbgV_JRE067LqRT9sUh1IpHkWfSMcZQ56pX9QZENIOs4qJu8-5XqXzVUnmmity1VBTPJrttcHFNglM_jBE5-O2LC97kfeEm5yRfX8mmGXB1sr656U8WLHPZtbVzmre8HHdIqgqG0Tk5PyC8r6erFdQbzr2N2QPQQDU1r52BAxZM5lCmKTiNOWaaq_3OTK8XHS4hPFGk616oKViD0azZrfxb1m44g2iPt13GICJWImLCYhDxBG1TFkQwy28vvh2vvo9ptkxvfdu_YDfJ--OCt57jfjdn4rqc76AXJubAC42Vl2gmq130fMJEuYsOTP4K_ogn-sNm5n-FWosqDKjCE1ThCaqwQhXWqMJ5hRWqsIHAZ7zAPaZwM0iymMJdrXpJbDGFFaZeo59fj8-_nLqmXIeb0oh1bgaep8czkpJ5RkWU0Dm4viL1RSJoEHq-l4HljURCRSJDj0PoLggXHkkhhuCEiKM3aKuqK7mHcBIeeZTTTFBGwJJzLsDrD0QWcUo4E8k-IvbtxqnhslclVYr4Qa3uo09Dl0YTufztx75VWWxek_YwY4Dfw93ePmaMd-jZ-IG8R1tdu5YH6Gm66fKr9tBg7w9cJa1P |
| linkProvider | Elsevier |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Parallel+and+distributed+association+rule+mining+in+life+science%3A+A+novel+parallel+algorithm+to+mine+genomics+data&rft.jtitle=Information+sciences&rft.au=Agapito%2C+Giuseppe&rft.au=Guzzi%2C+Pietro+Hiram&rft.au=Cannataro%2C+Mario&rft.date=2021-10-01&rft.issn=0020-0255&rft.volume=575&rft.spage=747&rft.epage=761&rft_id=info:doi/10.1016%2Fj.ins.2018.07.055&rft.externalDBID=n%2Fa&rft.externalDocID=10_1016_j_ins_2018_07_055 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0020-0255&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0020-0255&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0020-0255&client=summon |