HFIM: a Spark-based hybrid frequent itemset mining algorithm for big data processing

Frequent itemset mining is one of the data mining techniques applied to discover frequent patterns, used in prediction, association rule mining, classification, etc. Apriori algorithm is an iterative algorithm, which is used to find frequent itemsets from transactional dataset. It scans complete dat...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	The Journal of supercomputing Jg. 73; H. 8; S. 3652 - 3668
Hauptverfasser:	Sethi, Krishan Kumar, Ramesh, Dharavath
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York Springer US 01.08.2017 Springer Nature B.V
Schlagworte:	Algorithms Big Data Compilers Computer Science Data management Data mining Data processing Datasets Interpreters Iterative algorithms Processor Architectures Programming Languages Scanning Frequent pattern mining Apriori algorithm Big data Apache Spark
ISSN:	0920-8542, 1573-0484
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Abstract	Frequent itemset mining is one of the data mining techniques applied to discover frequent patterns, used in prediction, association rule mining, classification, etc. Apriori algorithm is an iterative algorithm, which is used to find frequent itemsets from transactional dataset. It scans complete dataset in each iteration to generate the large frequent itemsets of different cardinality, which seems better for small data but not feasible for big data. The MapReduce framework provides the distributed environment to run the Apriori on big transactional data. However, MapReduce is not suitable for iterative process and declines the performance. We introduce a novel algorithm named Hybrid Frequent Itemset Mining (HFIM), which utilizes the vertical layout of dataset to solve the problem of scanning the dataset in each iteration. Vertical dataset carries information to find support of each itemsets. Moreover, we also include some enhancements to reduce number of candidate itemsets. The proposed algorithm is implemented over Spark framework, which incorporates the concept of resilient distributed datasets and performs in-memory processing to optimize the execution time of operation. We compare the performance of HFIM with another Spark-based implementation of Apriori algorithm for various datasets. Experimental results show that the HFIM performs better in terms of execution time and space consumption.
AbstractList	Frequent itemset mining is one of the data mining techniques applied to discover frequent patterns, used in prediction, association rule mining, classification, etc. Apriori algorithm is an iterative algorithm, which is used to find frequent itemsets from transactional dataset. It scans complete dataset in each iteration to generate the large frequent itemsets of different cardinality, which seems better for small data but not feasible for big data. The MapReduce framework provides the distributed environment to run the Apriori on big transactional data. However, MapReduce is not suitable for iterative process and declines the performance. We introduce a novel algorithm named Hybrid Frequent Itemset Mining (HFIM), which utilizes the vertical layout of dataset to solve the problem of scanning the dataset in each iteration. Vertical dataset carries information to find support of each itemsets. Moreover, we also include some enhancements to reduce number of candidate itemsets. The proposed algorithm is implemented over Spark framework, which incorporates the concept of resilient distributed datasets and performs in-memory processing to optimize the execution time of operation. We compare the performance of HFIM with another Spark-based implementation of Apriori algorithm for various datasets. Experimental results show that the HFIM performs better in terms of execution time and space consumption.
Author	Ramesh, Dharavath Sethi, Krishan Kumar
Author_xml	– sequence: 1 givenname: Krishan Kumar surname: Sethi fullname: Sethi, Krishan Kumar organization: Department of Computer Science and Engineering, Indian Institute of Technology (ISM) – sequence: 2 givenname: Dharavath orcidid: 0000-0003-3338-6520 surname: Ramesh fullname: Ramesh, Dharavath email: ramesh.d.in@ieee.org organization: Department of Computer Science and Engineering, Indian Institute of Technology (ISM)
BookMark	eNp9kMFOAjEQhhuDiYA-gLcmnqudtrttvRkiYoLxIJ6b7m4XirCLbTnw9pbgwZjoaS7fN__MP0KDru8cQtdAb4FSeRcBGJOEgiSgS07EGRpCITmhQokBGlLNKFGFYBdoFOOaUiq45EO0mE2fX-6xxW87Gz5IZaNr8OpQBd_gNrjPvesS9slto0t46zvfLbHdLPvg02qL2z7gyi9xY5PFu9DXLsZMXKLz1m6iu_qeY_Q-fVxMZmT--vQ8eZiTmkOZiGZKVAWVlebOKSgaLgttS16Xjpc1ZaUWDiRUulWKN1RJaKv8RqOaWqoGOB-jm9PeHJ0vjcms-33ocqQBzYCB0KrIlDxRdehjDK41tU82-b5LwfqNAWqOFZpThSZXaI4VGpFN-GXugt_acPjXYScnZrZbuvDjpj-lL3ZOg8o
CitedBy_id	crossref_primary_10_1016_j_jksuci_2019_09_006 crossref_primary_10_1007_s10115_020_01464_1 crossref_primary_10_1016_j_patrec_2019_05_003 crossref_primary_10_1109_ACCESS_2022_3179592 crossref_primary_10_1007_s10586_018_2075_5 crossref_primary_10_1016_j_engappai_2020_103933 crossref_primary_10_1007_s11227_020_03253_7 crossref_primary_10_1007_s10586_018_2100_8 crossref_primary_10_1080_0144929X_2019_1626486 crossref_primary_10_1007_s11235_022_00882_6 crossref_primary_10_1016_j_engappai_2022_105480 crossref_primary_10_1007_s10586_024_04868_8 crossref_primary_10_1007_s12083_020_00980_9 crossref_primary_10_1155_2021_6688463 crossref_primary_10_3390_data7010011 crossref_primary_10_1016_j_jksuci_2020_09_010 crossref_primary_10_1007_s10586_018_1811_1 crossref_primary_10_1007_s10586_018_1812_0 crossref_primary_10_1007_s00530_020_00725_x crossref_primary_10_1007_s11227_020_03247_5 crossref_primary_10_1007_s10586_018_2420_8 crossref_primary_10_1155_2024_8781318 crossref_primary_10_1007_s41870_019_00337_3 crossref_primary_10_1016_j_future_2019_09_041 crossref_primary_10_1007_s10586_022_03673_5 crossref_primary_10_1007_s10586_023_04014_w
Cites_doi	10.1016/j.is.2014.07.006 10.1007/s00371-013-0886-1 10.1145/276305.276313 10.1016/j.ins.2014.01.015 10.1109/MIS.2010.47 10.1145/1327452.1327492 10.1109/CSCWD.2015.7230970 10.1109/ICCAE.2010.5451977 10.1007/978-3-540-28633-2_131 10.1007/978-1-4615-5669-5_1 10.1109/ICICIS.2010.5534718 10.1145/2184751.2184842 10.1109/ICSESS.2014.6933531 10.1145/1272996.1273005 10.1109/ITNG.2008.33 10.1109/IPDPSW.2014.185 10.1109/BigData.6691742 10.1109/SNPD.2012.31 10.1109/FCST.2015.28 10.1049/cp.2014.1525 10.1145/2809890.2809893
ContentType	Journal Article
Copyright	Springer Science+Business Media New York 2017 Copyright Springer Science & Business Media 2017
Copyright_xml	– notice: Springer Science+Business Media New York 2017 – notice: Copyright Springer Science & Business Media 2017
DBID	AAYXX CITATION JQ2
DOI	10.1007/s11227-017-1963-4
DatabaseName	CrossRef ProQuest Computer Science Collection
DatabaseTitle	CrossRef ProQuest Computer Science Collection
DatabaseTitleList	ProQuest Computer Science Collection
DeliveryMethod	fulltext_linktorsrc
Discipline	Computer Science
EISSN	1573-0484
EndPage	3668
ExternalDocumentID	10_1007_s11227_017_1963_4
GroupedDBID	-4Z -59 -5G -BR -EM -Y2 -~C .4S .86 .DC .VR 06D 0R~ 0VY 123 199 1N0 1SB 2.D 203 28- 29L 2J2 2JN 2JY 2KG 2KM 2LR 2P1 2VQ 2~H 30V 4.4 406 408 409 40D 40E 5QI 5VS 67Z 6NX 78A 8TC 8UJ 95- 95. 95~ 96X AAAVM AABHQ AACDK AAHNG AAIAL AAJBT AAJKR AANZL AAOBN AARHV AARTL AASML AATNV AATVU AAUYE AAWCG AAYIU AAYOK AAYQN AAYTO AAYZH ABAKF ABBBX ABBXA ABDBF ABDPE ABDZT ABECU ABFTD ABFTV ABHLI ABHQN ABJNI ABJOX ABKCH ABKTR ABMNI ABMQK ABNWP ABQBU ABQSL ABSXP ABTEG ABTHY ABTKH ABTMW ABULA ABWNU ABXPI ACAOD ACBXY ACDTI ACGFS ACHSB ACHXU ACKNC ACMDZ ACMLO ACOKC ACOMO ACPIV ACUHS ACZOJ ADHHG ADHIR ADIMF ADINQ ADKNI ADKPE ADMLS ADQRH ADRFC ADTPH ADURQ ADYFF ADZKW AEBTG AEFIE AEFQL AEGAL AEGNC AEJHL AEJRE AEKMD AEMSY AENEX AEOHA AEPYU AESKC AETLH AEVLU AEXYK AFBBN AFEXP AFGCZ AFLOW AFQWF AFWTZ AFZKB AGAYW AGDGC AGGDS AGJBK AGMZJ AGQEE AGQMX AGRTI AGWIL AGWZB AGYKE AHAVH AHBYD AHSBF AHYZX AI. AIAKS AIGIU AIIXL AILAN AITGF AJBLW AJRNO AJZVZ ALMA_UNASSIGNED_HOLDINGS ALWAN AMKLP AMXSW AMYLF AMYQR AOCGG ARCSS ARMRJ ASPBG AVWKF AXYYD AYJHY AZFZN B-. B0M BA0 BBWZM BDATZ BGNMA BSONS CAG COF CS3 CSCUP DDRTE DL5 DNIVK DPUIP DU5 EAD EAP EAS EBD EBLON EBS EDO EIOEI EJD EMK EPL ESBYG ESX F5P FEDTE FERAY FFXSO FIGPU FINBP FNLPD FRRFC FSGXE FWDCC GGCAI GGRSB GJIRD GNWQR GQ6 GQ7 GQ8 GXS H13 HF~ HG5 HG6 HMJXF HQYDN HRMNR HVGLF HZ~ H~9 I-F I09 IHE IJ- IKXTQ ITM IWAJR IXC IZIGR IZQ I~X I~Z J-C J0Z JBSCW JCJTX JZLTJ KDC KOV KOW LAK LLZTM M4Y MA- N2Q N9A NB0 NDZJH NPVJJ NQJWS NU0 O9- O93 O9G O9I O9J OAM OVD P19 P2P P9O PF0 PT4 PT5 QOK QOS R4E R89 R9I RHV RNI ROL RPX RSV RZC RZE RZK S16 S1Z S26 S27 S28 S3B SAP SCJ SCLPG SCO SDH SDM SHX SISQX SJYHP SNE SNPRN SNX SOHCF SOJ SPISZ SRMVM SSLCW STPWE SZN T13 T16 TEORI TSG TSK TSV TUC TUS U2A UG4 UOJIU UTJUX UZXMN VC2 VFIZW VH1 W23 W48 WH7 WK8 YLTOR Z45 Z7R Z7X Z7Z Z83 Z88 Z8M Z8N Z8R Z8T Z8W Z92 ZMTXR ~8M ~EX AAPKM AAYXX ABBRH ABDBE ABFSG ABJCF ABRTQ ACSTC ADHKG ADKFA AEZWR AFDZB AFFHD AFHIU AFKRA AFOHR AGQPQ AHPBZ AHWEU AIXLP ARAPS ATHPR AYFIA BENPR BGLVJ CCPQU CITATION HCIFZ K7- M7S PHGZM PHGZT PQGLB PTHSS JQ2
ID	FETCH-LOGICAL-c316t-9284b507b93ee815d3759a63c6e36c02694e171b9f883d0871fb920d8dc78d133
IEDL.DBID	RSV
ISICitedReferencesCount	38
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000406215500019&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN	0920-8542
IngestDate	Thu Sep 25 01:03:35 EDT 2025 Sat Nov 29 06:13:11 EST 2025 Tue Nov 18 21:34:56 EST 2025 Fri Feb 21 02:27:32 EST 2025
IsPeerReviewed	true
IsScholarly	true
Issue	8
Keywords	Frequent pattern mining Apriori algorithm Big data Apache Spark
Language	English
LinkModel	DirectLink
MergedId	FETCHMERGED-LOGICAL-c316t-9284b507b93ee815d3759a63c6e36c02694e171b9f883d0871fb920d8dc78d133
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14
ORCID	0000-0003-3338-6520
PQID	1921214985
PQPubID	2043774
PageCount	17
ParticipantIDs	proquest_journals_1921214985 crossref_citationtrail_10_1007_s11227_017_1963_4 crossref_primary_10_1007_s11227_017_1963_4 springer_journals_10_1007_s11227_017_1963_4
PublicationCentury	2000
PublicationDate	2017-08-01
PublicationDateYYYYMMDD	2017-08-01
PublicationDate_xml	– month: 08 year: 2017 text: 2017-08-01 day: 01
PublicationDecade	2010
PublicationPlace	New York
PublicationPlace_xml	– name: New York
PublicationSubtitle	An International Journal of High-Performance Computer Design, Analysis, and Use
PublicationTitle	The Journal of supercomputing
PublicationTitleAbbrev	J Supercomput
PublicationYear	2017
Publisher	Springer US Springer Nature B.V
Publisher_xml	– name: Springer US – name: Springer Nature B.V
References	Dean, Ghemawat (CR11) 2008; 51 Han, Kamber, Pei (CR3) 2011 CR19 CR18 CR39 CR38 Hashem, Yaqoob, Anuar, Mokhtar, Gani, Khan (CR1) 2015; 47 CR15 CR37 CR14 CR36 CR13 CR35 CR12 CR34 CR33 CR10 CR32 CR31 CR30 Bayardo (CR5) 1998; 27 Philip Chen, Zhang (CR2) 2014; 275 CR4 Jafri, Ali, Arabnia, Fatima (CR16) 2014; 30 CR8 CR7 CR29 CR28 CR9 CR27 Pacheco (CR6) 1997 CR26 CR25 CR24 CR23 CR21 CR20 Ye, Chiang (CR22) 2006; 2006 Arabnia, Fang, Lee, Zhang (CR17) 2010; 25 1963_CR38 1963_CR39 1963_CR14 1963_CR36 1963_CR15 1963_CR37 1963_CR12 1963_CR34 1963_CR13 1963_CR35 1963_CR10 1963_CR32 1963_CR33 1963_CR30 PS Pacheco (1963_CR6) 1997 1963_CR31 R Jafri (1963_CR16) 2014; 30 J Dean (1963_CR11) 2008; 51 IAT Hashem (1963_CR1) 2015; 47 CL Philip Chen (1963_CR2) 2014; 275 1963_CR18 1963_CR19 1963_CR27 J Han (1963_CR3) 2011 1963_CR28 1963_CR25 1963_CR26 1963_CR23 HR Arabnia (1963_CR17) 2010; 25 1963_CR24 1963_CR21 1963_CR20 Y Ye (1963_CR22) 2006; 2006 1963_CR9 1963_CR8 1963_CR7 1963_CR4 RJ Bayardo Jr (1963_CR5) 1998; 27 1963_CR29
References_xml	– volume: 2006 start-page: 87 year: 2006 end-page: 94 ident: CR22 article-title: A parallel Apriori algorithm for frequent itemsets mining publication-title: In Proceedings of Fourth International Conference Software Engineering Research Management and applications SERA – ident: CR18 – ident: CR4 – ident: CR14 – ident: CR39 – volume: 47 start-page: 98 year: 2015 end-page: 115 ident: CR1 article-title: The rise of “big data” on cloud computing: review and open research issues publication-title: Infor Syst doi: 10.1016/j.is.2014.07.006 – ident: CR37 – ident: CR12 – ident: CR30 – ident: CR10 – ident: CR33 – ident: CR35 – ident: CR29 – volume: 30 start-page: 1197 issue: 11 year: 2014 end-page: 1222 ident: CR16 article-title: Computer vision-based object recognition for the visually impaired in an indoors environment: a survey publication-title: Vis Comput doi: 10.1007/s00371-013-0886-1 – ident: CR8 – ident: CR25 – ident: CR27 – ident: CR23 – volume: 27 start-page: 85 issue: 2 year: 1998 end-page: 93 ident: CR5 article-title: Efficiently mining long patterns from databases publication-title: ACM Sigmod Record doi: 10.1145/276305.276313 – volume: 275 start-page: 314 year: 2014 end-page: 347 ident: CR2 article-title: Data-intensive applications, challenges, techniques and technologies: a survey on big data publication-title: Inf. Sci. doi: 10.1016/j.ins.2014.01.015 – ident: CR21 – ident: CR19 – ident: CR15 – ident: CR38 – ident: CR31 – ident: CR13 – volume: 25 start-page: 10 issue: 2 year: 2010 end-page: 11 ident: CR17 article-title: Context-aware middleware and intelligent agents for smart environments publication-title: IEEE Intell Syst doi: 10.1109/MIS.2010.47 – ident: CR9 – year: 1997 ident: CR6 publication-title: Parallel programming with MPI – volume: 51 start-page: 107 issue: 1 year: 2008 end-page: 113 ident: CR11 article-title: MapReduce: simplified data processing on large clusters publication-title: Commun ACM doi: 10.1145/1327452.1327492 – ident: CR32 – ident: CR34 – ident: CR36 – ident: CR7 – ident: CR28 – ident: CR26 – ident: CR24 – ident: CR20 – year: 2011 ident: CR3 publication-title: Data mining: concepts and techniques – ident: 1963_CR34 doi: 10.1109/CSCWD.2015.7230970 – ident: 1963_CR23 – ident: 1963_CR9 – volume: 25 start-page: 10 issue: 2 year: 2010 ident: 1963_CR17 publication-title: IEEE Intell Syst doi: 10.1109/MIS.2010.47 – ident: 1963_CR20 doi: 10.1109/ICCAE.2010.5451977 – ident: 1963_CR7 – ident: 1963_CR18 doi: 10.1007/978-3-540-28633-2_131 – ident: 1963_CR21 – ident: 1963_CR35 doi: 10.1007/978-1-4615-5669-5_1 – volume-title: Parallel programming with MPI year: 1997 ident: 1963_CR6 – ident: 1963_CR37 – ident: 1963_CR12 – ident: 1963_CR14 – volume: 30 start-page: 1197 issue: 11 year: 2014 ident: 1963_CR16 publication-title: Vis Comput doi: 10.1007/s00371-013-0886-1 – ident: 1963_CR4 – ident: 1963_CR10 – ident: 1963_CR30 doi: 10.1109/ICICIS.2010.5534718 – ident: 1963_CR25 doi: 10.1145/2184751.2184842 – volume: 47 start-page: 98 year: 2015 ident: 1963_CR1 publication-title: Infor Syst doi: 10.1016/j.is.2014.07.006 – ident: 1963_CR29 doi: 10.1109/ICSESS.2014.6933531 – ident: 1963_CR8 doi: 10.1145/1272996.1273005 – ident: 1963_CR19 doi: 10.1109/ITNG.2008.33 – ident: 1963_CR39 – volume: 27 start-page: 85 issue: 2 year: 1998 ident: 1963_CR5 publication-title: ACM Sigmod Record doi: 10.1145/276305.276313 – ident: 1963_CR24 – ident: 1963_CR31 doi: 10.1109/IPDPSW.2014.185 – ident: 1963_CR28 doi: 10.1109/BigData.6691742 – volume: 275 start-page: 314 year: 2014 ident: 1963_CR2 publication-title: Inf. Sci. doi: 10.1016/j.ins.2014.01.015 – volume: 51 start-page: 107 issue: 1 year: 2008 ident: 1963_CR11 publication-title: Commun ACM doi: 10.1145/1327452.1327492 – ident: 1963_CR26 doi: 10.1109/SNPD.2012.31 – ident: 1963_CR13 – ident: 1963_CR32 doi: 10.1109/FCST.2015.28 – ident: 1963_CR15 – ident: 1963_CR27 doi: 10.1049/cp.2014.1525 – volume: 2006 start-page: 87 year: 2006 ident: 1963_CR22 publication-title: In Proceedings of Fourth International Conference Software Engineering Research Management and applications SERA – ident: 1963_CR33 doi: 10.1145/2809890.2809893 – volume-title: Data mining: concepts and techniques year: 2011 ident: 1963_CR3 – ident: 1963_CR36 – ident: 1963_CR38
SSID	ssj0004373
Score	2.31999
Snippet	Frequent itemset mining is one of the data mining techniques applied to discover frequent patterns, used in prediction, association rule mining,...
SourceID	proquest crossref springer
SourceType	Aggregation Database Enrichment Source Index Database Publisher
StartPage	3652
SubjectTerms	Algorithms Big Data Compilers Computer Science Data management Data mining Data processing Datasets Interpreters Iterative algorithms Processor Architectures Programming Languages Scanning
Title	HFIM: a Spark-based hybrid frequent itemset mining algorithm for big data processing
URI	https://link.springer.com/article/10.1007/s11227-017-1963-4 https://www.proquest.com/docview/1921214985
Volume	73
WOSCitedRecordID	wos000406215500019&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: PRVAVX databaseName: SpringerLINK Contemporary 1997-Present customDbUrl: eissn: 1573-0484 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0004373 issn: 0920-8542 databaseCode: RSV dateStart: 19970101 isFulltext: true titleUrlDefault: https://link.springer.com/search?facet-content-type=%22Journal%22 providerName: Springer Nature
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV3NS8MwFH_o9ODF-YnTKTl4UgJtk7apNxHHPDiETdmtpEk6h_uiq4L_vUnaWhUV9NzXEF5e8t6P9_EDONUIw5OCOzhRMsA0UBJzEflYCkKZ1M-hotKSTYS9HhsOo7uyj3tZVbtXKUn7UtfNbq7nmTLJEBurwXQV1nwzbMZA9P5D3QxJirRypHER86lXpTK_W-KzM6ojzC9JUetrOs1_7XILNsvQEl0WtrANK2q2A82KtgGVt3gXBt3Oze0F4qi_4NkTNn5MosdX07qF0syWVufIMrSpHE0tgQTik9E8G-ePU6SDXJSMR8iUlqJF0WagJfbgvnM9uOriklwBC-IGOY60X0p0MJhERCnm-pKEfsQDIgJFAuGYBlflhm4SpYwR6WhclSZapZJJETKpke0-NGbzmToAZHKlKZWcpExQT_lcUmqmWvGIaLykRAucSsuxKCePGwKMSVzPTDZai7XWYqO1mLbg7P2XRTF24zfhdnV0cXkDl7GZ8-Zp-Mf8FpxXR_Xh80-LHf5J-gg2PHPWtiKwDY08e1bHsC5e8vEyO7GG-QaN-Ns9
linkProvider	Springer Nature
linkToHtml	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV3dS8MwED90Cvri_MTp1Dz4pATWJm0T30QcG84hOGVvJU3Sbbgvuir435t0rVNRQZ97DeFyyd2P-_gBnBqE4SopajjSysfU1woLyT2sJKFMmedQU5WRTQTtNut2-V3exz0rqt2LlGT2Ui-a3RzXtWWSAbZWg-kyrFDLsmMh-v3johmSzNPK3OAi5lG3SGV-t8RnZ7SIML8kRTNfUy__a5ebsJGHluhybgtbsKTH21AuaBtQfot3oNOoN28vkED3U5E8YevHFOq_2tYtFCdZaXWKMoY2naJRRiCBxLA3SQZpf4RMkIuiQQ_Z0lI0nbcZGIldeKhfd64aOCdXwJI4foq58UuRCQYjTrRmjqdI4HHhE-lr4suabXDVTuBEPGaMqJrBVXFkVKqYkgFTBtnuQWk8Get9QDZXGlMlSMwkdbUnFKV2qpXgxOAlLStQK7QcynzyuCXAGIaLmclWa6HRWmi1FtIKnL3_Mp2P3fhNuFocXZjfwFlo57wZ6-DMq8B5cVQfPv-02MGfpE9grdG5bYWtZvvmENZde-5ZdWAVSmnyrI9gVb6kg1lynBnpG91l3iE
linkToPdf	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1LS8NAEB58IV58i_W5B0_K0ia7STbeRC0WtRRbpbew2d20xTaWNAr-e3fzsCoqiOdMljAzm5mPeXwARxph2FLwGg6VdDF1lcRc-A6WglAm9e9QUZmRTXjNJut2_VbBczopu93LkmQ-02C2NMVpdSyj6nTwzbJt0zLpYeNBmM7CPNVAxvR03bUfpoORJC8x-xojMYfaZVnzuyM-B6ZptvmlQJrFnfrKv794FZaLlBOd5T6yBjMqXoeVks4BFbd7AzpX9cbtKeKoPebJIzbxTaL-qxnpQlGStVynKGNuUykaZcQSiA97T8kg7Y-QTn5ROOgh03KKxvn4gZbYhPv6Zef8ChekC1gQy02xr-NVqJPE0CdKMcuRxHN87hLhKuKKmhl8VZZnhX7EGJE1jbeiUKtXMik8JjXi3YK5-ClW24BMDTWikpOICWorh0tKzbYr7hONo5SoQK3UeCCKjeSGGGMYTHcpG60FWmuB0VpAK3D8_so4X8fxm_BeacaguJmTwOx_szUsZE4FTkqzfXj802E7f5I-hMXWRT24aTSvd2HJNmbPmgb3YC5NntU-LIiXdDBJDjJ_fQNAZecF
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=HFIM%3A+a+Spark-based+hybrid+frequent+itemset+mining+algorithm+for+big+data+processing&rft.jtitle=The+Journal+of+supercomputing&rft.au=Sethi%2C+Krishan+Kumar&rft.au=Ramesh%2C+Dharavath&rft.date=2017-08-01&rft.issn=0920-8542&rft.eissn=1573-0484&rft.volume=73&rft.issue=8&rft.spage=3652&rft.epage=3668&rft_id=info:doi/10.1007%2Fs11227-017-1963-4&rft.externalDBID=n%2Fa&rft.externalDocID=10_1007_s11227_017_1963_4
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0920-8542&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0920-8542&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0920-8542&client=summon