Performance of Parallel K-Means Algorithms in Java
K-means is a well-known clustering algorithm often used for its simplicity and potential efficiency. Its properties and limitations have been investigated by many works reported in the literature. K-means, though, suffers from computational problems when dealing with large datasets with many dimensi...
Uloženo v:
| Vydáno v: | Algorithms Ročník 15; číslo 4; s. 117 |
|---|---|
| Hlavní autor: | |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Basel
MDPI AG
01.04.2022
|
| Témata: | |
| ISSN: | 1999-4893, 1999-4893 |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| Abstract | K-means is a well-known clustering algorithm often used for its simplicity and potential efficiency. Its properties and limitations have been investigated by many works reported in the literature. K-means, though, suffers from computational problems when dealing with large datasets with many dimensions and great number of clusters. Therefore, many authors have proposed and experimented different techniques for the parallel execution of K-means. This paper describes a novel approach to parallel K-means which, today, is based on commodity multicore machines with shared memory. Two reference implementations in Java are developed and their performances are compared. The first one is structured according to a map/reduce schema that leverages the built-in multi-threaded concurrency automatically provided by Java to parallel streams. The second one, allocated on the available cores, exploits the parallel programming model of the Theatre actor system, which is control-based, totally lock-free, and purposely relies on threads as coarse-grain “programming-in-the-large” units. The experimental results confirm that some good execution performance can be achieved through the implicit and intuitive use of Java concurrency in parallel streams. However, better execution performance can be guaranteed by the modular Theatre implementation which proves more adequate for an exploitation of the computational resources. |
|---|---|
| AbstractList | K-means is a well-known clustering algorithm often used for its simplicity and potential efficiency. Its properties and limitations have been investigated by many works reported in the literature. K-means, though, suffers from computational problems when dealing with large datasets with many dimensions and great number of clusters. Therefore, many authors have proposed and experimented different techniques for the parallel execution of K-means. This paper describes a novel approach to parallel K-means which, today, is based on commodity multicore machines with shared memory. Two reference implementations in Java are developed and their performances are compared. The first one is structured according to a map/reduce schema that leverages the built-in multi-threaded concurrency automatically provided by Java to parallel streams. The second one, allocated on the available cores, exploits the parallel programming model of the Theatre actor system, which is control-based, totally lock-free, and purposely relies on threads as coarse-grain “programming-in-the-large” units. The experimental results confirm that some good execution performance can be achieved through the implicit and intuitive use of Java concurrency in parallel streams. However, better execution performance can be guaranteed by the modular Theatre implementation which proves more adequate for an exploitation of the computational resources. |
| Author | Nigro, Libero |
| Author_xml | – sequence: 1 givenname: Libero orcidid: 0000-0001-6577-4777 surname: Nigro fullname: Nigro, Libero |
| BookMark | eNplkEtPwzAQhC1UJNrCgX8QiROHUDvxIz5WFY9CET3A2do46-IqjYuTIvHvCRQQgtOOVjOjTzMigyY0SMgpoxd5rukEmKCcMqYOyJBprVNe6HzwSx-RUduuKZVCSzYk2RKjC3EDjcUkuGQJEeoa6-QuvUdo2mRar0L03fOmTXyT3MIrHJNDB3WLJ193TJ6uLh9nN-ni4Xo-my5Sm4uiS4uq0AKklJoXjrkyYygplKitcAAoMXNZXiprkbtCaEWrTJVKC-0KSvtAPibzfW8VYG220W8gvpkA3nw-QlwZiJ23NRqJlaik4lw55LbKoCpdUSpW2Zwh16rvOtt3bWN42WHbmXXYxabHN5kUWY8oJO9dk73LxtC2EZ2xvoPOh6aL4GvDqPkY2fyM3CfO_yS-Of973wH7cXwe |
| CitedBy_id | crossref_primary_10_3390_a16070349 crossref_primary_10_3390_a16120572 crossref_primary_10_3390_math13081285 crossref_primary_10_1016_j_simpat_2022_102712 |
| Cites_doi | 10.1007/s10994-021-06021-7 10.1109/TIT.1982.1056489 10.1016/j.patcog.2019.04.014 10.1109/PAAP.2011.17 10.1016/j.eswa.2012.07.021 10.3389/frai.2021.740817 10.1016/j.compeleceng.2017.12.002 10.1109/ICCIC.2013.6724291 10.1007/978-0-387-09766-4_125 10.1007/978-3-642-10665-1_71 10.1145/342009.335388 10.1109/ICSESS.2015.7339213 10.1016/j.patrec.2009.04.013 10.1016/j.simpat.2018.07.011 10.1002/cpe.3102 10.1016/j.simpat.2020.102189 10.1016/j.patrec.2009.09.011 10.1126/science.1242072 10.7551/mitpress/1086.001.0001 10.1007/s10489-018-1238-7 |
| ContentType | Journal Article |
| Copyright | 2022 by the author. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
| Copyright_xml | – notice: 2022 by the author. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
| DBID | AAYXX CITATION 3V. 7SC 7TB 7XB 8AL 8FD 8FE 8FG 8FK ABJCF ABUWG AFKRA ARAPS AZQEC BENPR BGLVJ CCPQU DWQXO FR3 GNUQQ HCIFZ JQ2 K7- KR7 L6V L7M L~C L~D M0N M7S P62 PHGZM PHGZT PIMPY PKEHL PQEST PQGLB PQQKQ PQUKI PRINS PTHSS Q9U DOA |
| DOI | 10.3390/a15040117 |
| DatabaseName | CrossRef ProQuest Central (Corporate) Computer and Information Systems Abstracts Mechanical & Transportation Engineering Abstracts ProQuest Central (purchase pre-March 2016) Computing Database (Alumni Edition) Technology Research Database ProQuest SciTech Collection ProQuest Technology Collection ProQuest Central (Alumni) (purchase pre-March 2016) Materials Science & Engineering Collection ProQuest Central (Alumni) ProQuest Central UK/Ireland ProQuest Advanced Technologies & Aerospace Collection ProQuest Central Essentials ProQuest Central Technology collection ProQuest One Community College ProQuest Central Engineering Research Database ProQuest Central Student SciTech Premium Collection ProQuest Computer Science Collection Computer Science Database Civil Engineering Abstracts ProQuest Engineering Collection Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional Computing Database Engineering Database ProQuest Advanced Technologies & Aerospace Collection ProQuest Databases ProQuest One Academic (New) Publicly Available Content Database ProQuest One Academic Middle East (New) ProQuest One Academic Eastern Edition (DO NOT USE) One Applied & Life Sciences ProQuest One Academic (retired) ProQuest One Academic UKI Edition ProQuest Central China Engineering Collection (ProQuest) ProQuest Central Basic DOAJ Directory of Open Access Journals |
| DatabaseTitle | CrossRef Publicly Available Content Database Computer Science Database ProQuest Central Student Technology Collection Technology Research Database Computer and Information Systems Abstracts – Academic ProQuest One Academic Middle East (New) Mechanical & Transportation Engineering Abstracts ProQuest Advanced Technologies & Aerospace Collection ProQuest Central Essentials ProQuest Computer Science Collection Computer and Information Systems Abstracts ProQuest Central (Alumni Edition) SciTech Premium Collection ProQuest One Community College ProQuest Central China ProQuest Central ProQuest One Applied & Life Sciences ProQuest Engineering Collection ProQuest Central Korea ProQuest Central (New) Advanced Technologies Database with Aerospace Engineering Collection Advanced Technologies & Aerospace Collection Civil Engineering Abstracts ProQuest Computing Engineering Database ProQuest Central Basic ProQuest Computing (Alumni Edition) ProQuest One Academic Eastern Edition ProQuest Technology Collection ProQuest SciTech Collection Computer and Information Systems Abstracts Professional ProQuest One Academic UKI Edition Materials Science & Engineering Collection Engineering Research Database ProQuest One Academic ProQuest One Academic (New) ProQuest Central (Alumni) |
| DatabaseTitleList | Publicly Available Content Database CrossRef |
| Database_xml | – sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: PIMPY name: Publicly Available Content Database url: http://search.proquest.com/publiccontent sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Computer Science |
| EISSN | 1999-4893 |
| ExternalDocumentID | oai_doaj_org_article_6ed5d67447fe4cd2adbf8b71dc31e497 10_3390_a15040117 |
| GroupedDBID | 23M 2WC 5VS 8FE 8FG AADQD AAFWJ AAYXX ABDBF ABJCF ABUWG ACUHS ADBBV AFFHD AFKRA AFPKN AFZYC ALMA_UNASSIGNED_HOLDINGS AMVHM ARAPS AZQEC BCNDV BENPR BGLVJ BPHCQ CCPQU CITATION DWQXO E3Z ESX GNUQQ GROUPED_DOAJ HCIFZ IAO J9A K6V K7- KQ8 L6V M7S MODMG M~E OK1 OVT P2P PHGZM PHGZT PIMPY PQGLB PQQKQ PROAC PTHSS TR2 TUS 3V. 7SC 7TB 7XB 8AL 8FD 8FK FR3 JQ2 KR7 L7M L~C L~D M0N P62 PKEHL PQEST PQUKI PRINS Q9U |
| ID | FETCH-LOGICAL-c358t-8d895a666948f1fb21e60abe9c5faae6e2f23b7cce4f85970d27b7959f800f1f3 |
| IEDL.DBID | DOA |
| ISICitedReferencesCount | 7 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000785313400001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 1999-4893 |
| IngestDate | Thu Dec 04 16:49:09 EST 2025 Mon Nov 17 23:13:01 EST 2025 Tue Nov 18 21:16:18 EST 2025 Sat Nov 29 07:11:10 EST 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 4 |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c358t-8d895a666948f1fb21e60abe9c5faae6e2f23b7cce4f85970d27b7959f800f1f3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| ORCID | 0000-0001-6577-4777 |
| OpenAccessLink | https://doaj.org/article/6ed5d67447fe4cd2adbf8b71dc31e497 |
| PQID | 2652948564 |
| PQPubID | 2032439 |
| ParticipantIDs | doaj_primary_oai_doaj_org_article_6ed5d67447fe4cd2adbf8b71dc31e497 proquest_journals_2652948564 crossref_citationtrail_10_3390_a15040117 crossref_primary_10_3390_a15040117 |
| PublicationCentury | 2000 |
| PublicationDate | 2022-04-01 |
| PublicationDateYYYYMMDD | 2022-04-01 |
| PublicationDate_xml | – month: 04 year: 2022 text: 2022-04-01 day: 01 |
| PublicationDecade | 2020 |
| PublicationPlace | Basel |
| PublicationPlace_xml | – name: Basel |
| PublicationTitle | Algorithms |
| PublicationYear | 2022 |
| Publisher | MDPI AG |
| Publisher_xml | – name: MDPI AG |
| References | Cuomo (ref_10) 2019; 75 Franti (ref_18) 2019; 93 ref_12 ref_11 Nigro (ref_20) 2018; 87 ref_30 Chaoji (ref_16) 2009; 30 ref_19 Yang (ref_28) 2021; 4 Gusev (ref_25) 2014; 26 Franti (ref_15) 2018; 48 ref_24 Kantabutra (ref_6) 2000; 1 ref_23 Rodriguez (ref_27) 2014; 344 Celebi (ref_17) 2013; 40 ref_22 Lloyd (ref_14) 1982; 28 Jain (ref_2) 2010; 31 ref_1 Nigro (ref_13) 2021; 106 ref_29 Cicirelli (ref_21) 2021; 25 ref_26 ref_9 ref_8 ref_5 Vouros (ref_3) 2021; 110 ref_4 ref_7 Ahmed (ref_31) 2011; 25 |
| References_xml | – volume: 110 start-page: 1975 year: 2021 ident: ref_3 article-title: An empirical comparison between stochastic and deterministic centroid initialisation for K-means variations publication-title: Mach. Learn. doi: 10.1007/s10994-021-06021-7 – volume: 28 start-page: 129 year: 1982 ident: ref_14 article-title: Least squares quantization in PCM publication-title: IEEE Trans. Inf. Theory doi: 10.1109/TIT.1982.1056489 – volume: 93 start-page: 95 year: 2019 ident: ref_18 article-title: How much can k-means be improved by using better initialization and repeats? publication-title: Pattern Recognit. doi: 10.1016/j.patcog.2019.04.014 – ident: ref_30 – ident: ref_5 doi: 10.1109/PAAP.2011.17 – volume: 40 start-page: 200 year: 2013 ident: ref_17 article-title: A comparative study of efficient initialization methods for the k-means clustering algorithm publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2012.07.021 – volume: 4 start-page: 740817 year: 2021 ident: ref_28 article-title: Adaptive initialization method for K-means algorithm publication-title: Front. Artif. Intell. doi: 10.3389/frai.2021.740817 – ident: ref_24 – ident: ref_26 – ident: ref_11 – volume: 75 start-page: 262 year: 2019 ident: ref_10 article-title: A GPU-accelerated parallel K-means algorithm publication-title: Comput. Electr. Eng. doi: 10.1016/j.compeleceng.2017.12.002 – ident: ref_9 doi: 10.1109/ICCIC.2013.6724291 – ident: ref_23 doi: 10.1007/978-0-387-09766-4_125 – ident: ref_7 doi: 10.1007/978-3-642-10665-1_71 – ident: ref_1 – volume: 25 start-page: 233 year: 2021 ident: ref_21 article-title: A development methodology for cyber-physical systems based on deterministic Theatre with hybrid actors publication-title: TASK Q. Spec. Issue Cyber-Phys. Syst. – ident: ref_19 doi: 10.1145/342009.335388 – ident: ref_29 doi: 10.1109/ICSESS.2015.7339213 – volume: 30 start-page: 994 year: 2009 ident: ref_16 article-title: Robust partitional clustering by outlier and density insensitive seeding publication-title: Pattern Recognit. Lett. doi: 10.1016/j.patrec.2009.04.013 – ident: ref_8 – ident: ref_4 – volume: 87 start-page: 343 year: 2018 ident: ref_20 article-title: Qualitative and quantitative model checking of distributed probabilistic timed actors publication-title: Simul. Model. Pract. Theory doi: 10.1016/j.simpat.2018.07.011 – volume: 26 start-page: 1847 year: 2014 ident: ref_25 article-title: A superlinear speedup region for matrix multiplication publication-title: Concurr. Comput. Pract. Exp. doi: 10.1002/cpe.3102 – ident: ref_12 – volume: 106 start-page: 102189 year: 2021 ident: ref_13 article-title: Parallel Theatre: A Java actor-framework for high-performance computing publication-title: Simul. Model. Pract. Theory doi: 10.1016/j.simpat.2020.102189 – volume: 31 start-page: 651 year: 2010 ident: ref_2 article-title: Data clustering: 50 years beyond k-means publication-title: Pattern Recognit. Lett. doi: 10.1016/j.patrec.2009.09.011 – volume: 344 start-page: 1492 year: 2014 ident: ref_27 article-title: Clustering by fast search and find of density peaks publication-title: Science doi: 10.1126/science.1242072 – ident: ref_22 doi: 10.7551/mitpress/1086.001.0001 – volume: 25 start-page: 1 year: 2011 ident: ref_31 article-title: An initialization method for the K-means algorithm using RNN and coupling degree publication-title: Int. J. Comput. Appl. – volume: 1 start-page: 243 year: 2000 ident: ref_6 article-title: Parallel K-means clustering algorithm on NOWs publication-title: NECTEC Tech. J. – volume: 48 start-page: 4743 year: 2018 ident: ref_15 article-title: K-means properties on six clustering benchmark datasets publication-title: Appl. Intell. doi: 10.1007/s10489-018-1238-7 |
| SSID | ssj0065961 |
| Score | 2.3156445 |
| Snippet | K-means is a well-known clustering algorithm often used for its simplicity and potential efficiency. Its properties and limitations have been investigated by... |
| SourceID | doaj proquest crossref |
| SourceType | Open Website Aggregation Database Enrichment Source Index Database |
| StartPage | 117 |
| SubjectTerms | Actors Algorithms Clustering Concurrency Datasets functional parallel streams High performance computing Java K-means clustering Methods multi-core machines Neighborhoods parallel algorithms Parallel programming Theater Theaters & cinemas |
| SummonAdditionalLinks | – databaseName: Engineering Database dbid: M7S link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV3NS8MwFA86PXhxfuJ0ShAPXsrWtPnoSaYoojgGKuxW0nzMwexmN_f3-9KlG6J48dqm4ZGXvLxf-vL7IXQhdMK1ou0glkoDQGE0EIrogGuWJRQyiFDYUmyCd7ui3096_sBt6ssqq5hYBmo9Vu6MvEUYJY7JhMVXk4_AqUa5v6teQmMdbTiWhLAs3XuuIjGjCQsXbEIRQPuWhOQndhxo3_agkqr_RyQut5e7-n8N20HbPrHEncVM2EVrJt9D9Uq0Afs1vI9Ib3VVAI8t7snCyamM8GPwZGDfwp3RALqfvb1P8TDHD3IuD9Dr3e3LzX3ghRMCFVExC4QWCZUATMAuG9qMhIa1ZWYSRa2UhhliSZRxpUxsBSCKtiY8c6LjFtJH-CA6RLV8nJsjhCOijIpjLgxAQa4gOVBGmlhxaSHXDGkDXVZDmSrPKu7ELUYpoAs36uly1BvofNl0sqDS-K3RtfPHsoFjvy4fjItB6hdTyoymmnEwy4IpmkidWZHxUKsoNHECnTQrV6V-SU7TlZ-O_359graIu-NQluc0UW1WfJpTtKnms-G0OCtn2BeBc9my priority: 102 providerName: ProQuest |
| Title | Performance of Parallel K-Means Algorithms in Java |
| URI | https://www.proquest.com/docview/2652948564 https://doaj.org/article/6ed5d67447fe4cd2adbf8b71dc31e497 |
| Volume | 15 |
| WOSCitedRecordID | wos000785313400001&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: PRVAON databaseName: DOAJ Directory of Open Access Journals customDbUrl: eissn: 1999-4893 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0065961 issn: 1999-4893 databaseCode: DOA dateStart: 20080101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources customDbUrl: eissn: 1999-4893 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0065961 issn: 1999-4893 databaseCode: M~E dateStart: 20080101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre – providerCode: PRVPQU databaseName: Computer Science Database customDbUrl: eissn: 1999-4893 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0065961 issn: 1999-4893 databaseCode: K7- dateStart: 20080301 isFulltext: true titleUrlDefault: http://search.proquest.com/compscijour providerName: ProQuest – providerCode: PRVPQU databaseName: Engineering Database customDbUrl: eissn: 1999-4893 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0065961 issn: 1999-4893 databaseCode: M7S dateStart: 20080301 isFulltext: true titleUrlDefault: http://search.proquest.com providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: eissn: 1999-4893 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0065961 issn: 1999-4893 databaseCode: BENPR dateStart: 20080301 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVPQU databaseName: Publicly Available Content Database customDbUrl: eissn: 1999-4893 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0065961 issn: 1999-4893 databaseCode: PIMPY dateStart: 20080301 isFulltext: true titleUrlDefault: http://search.proquest.com/publiccontent providerName: ProQuest |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV3LSsNAFB1EXbjxLVZrGcSFm2AzmedSpcUHluAD6ipM5qGF2kpbu_TbvZOkVVFw42YWyYSEczNz74E75yB0JK0S1rBmRLWxQFA4i6QhNhKW54pBBRFLX5hNiE5Hdrsq_WL1FXrCSnngErgT7iyzXFAqvKPGEm1zL3MRW5PEjqriHHlTqBmZKvdgzhSPSx2hBEj9iYayhwb1s2_ZpxDp_7EHF4mlvY5Wq4oQn5ZfsoEW3GATrc3cFnC1-LYQST97_PHQ41SPgg9KH19HNw4SDj7tPw2B6j-_jHFvgK_0VG-jh3br_vwiqhwPIpMwOYmklYppYBSKSh_7nMSON3XulGFea8cd8STJhTGOeglUoGmJyINbuIe6Dx5IdtDiYDhwuwgnxDgDqEkHHE4YyOrGaUBQaA9FYsxq6HiGRGYqOfDgStHPgBYE0LI5aDV0OJ_6Wmpg_DbpLMA5nxBkq4sLEMysCmb2VzBrqD4LRlatpXFGOCNBw4bTvf94xz5aIeEIQ9F9U0eLk9GbO0DLZjrpjUcNtHTW6qS3jeJ3gvFaRI3QD3oXxvcW3E8vb9LHD0GA0xk |
| linkProvider | Directory of Open Access Journals |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1LT9tAEB4BRYILtDwEFNpVVSQuK-L1vnxACGgRNBDlQCVuZr0PQAoJJAHUP8VvZNaxgxBVbxx6tdersWf8zTf27nwA37XLlLOiQbmxDgsUKai2zFHlZJEJZBCJDqXYhGq19Pl51p6Ap3ovTFxWWWNiCdSuZ-M38m0mBYudTCTfvb2jUTUq_l2tJTRGYdH0fx6xZBvsHP9A_24ydvjz7OCIVqoC1KZCD6l2OhMGWTvOFpJQsMTLhil8ZkUwxkvPAksLZa3nQSPdbjimiqjIHZBb4QUpzjsJH3iqVXyvmorWyC9FJpNR96I0zRrbBskWjz3XXuW8UhrgDfKX6exw_n97EB9hriLOZG8U6Z9gwncXYL4WpSAVRi0Ca79shSC9QNqmH-ViOqRJTz3mZbLXucTbGV7dDMh1l_wyD2YJfr-L5csw1e11_QqQlFlvOVfaY6mrLJIf643nVpmAXDoRq7BVuy63Vdf0KN7RybF6il7Ox15ehW_jobejViF_G7Qf_T8eELt7lwd6_cu8AotceiecVGhWQFMcM64IulCJs2nieYaTrNehkVeQM8hf4mLt36e_wszR2elJfnLcan6GWRb3c5RLkdZhati_9xswbR-G14P-lzK6CVy8dxQ9A6R8N64 |
| linkToPdf | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1LT9tAEB7RgKpeSnlUpQW6QiD1YiVe78sHhHg0IgQiH6gEJ7PeB0VKE5qkVP1r_DpmHTsIteqNA1d7vVp7Ps98Y-_MB7CtbCqt4a2IaWMxQRE8UobaSFpRpBwZRKx8KTYhez11cZFmc3Bf18KEbZW1TywdtR2a8I28SQWnoZOJYE1fbYvIjtp7tz-joCAV_rTWchpTiHTdn9-Yvo13O0do6x1K21_PD4-jSmEgMglXk0hZlXKNDB5n9rEvaOxESxcuNdxr7YSjniaFNMYxr5B6tyyVRVDn9siz8IIE530F80jJGW3AfNY5yy7rOCB4KuJpL6MkSVtNjdSLhQ5sTyJgKRTwVxwog1t78SU_lnfwtqLUZH_6DizBnBssw2ItV0Eq77UCNHsskiBDTzI9CkIyfdKNzhxGbLLfv8bbmXz_MSY3A3Ki7_QqfHuWlb-HxmA4cB-AJNQ4w5hUDpNgaZAWGacdM1J7ZNkxX4MvtRlzU_VTD7Ie_RzzqmDxfGbxNdiaDb2dNhH516CDgIXZgND3uzwwHF3nlRvJhbPcConL8rgUS7UtvCpkbE0SO5biJOs1TPLKGY3zR4x8_P_pz_AawZOfdnrdT_CGhkKPco_SOjQmo19uAxbM3eRmPNqsoE7g6rlh9AAEk0Iv |
| 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=Performance+of+Parallel+K-Means+Algorithms+in+Java&rft.jtitle=Algorithms&rft.au=Nigro%2C+Libero&rft.date=2022-04-01&rft.issn=1999-4893&rft.eissn=1999-4893&rft.volume=15&rft.issue=4&rft.spage=117&rft_id=info:doi/10.3390%2Fa15040117&rft.externalDBID=n%2Fa&rft.externalDocID=10_3390_a15040117 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1999-4893&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1999-4893&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1999-4893&client=summon |