Optimizing Euclidean Distance Computation
This paper presents a comparative analysis of seventeen different approaches to optimizing Euclidean distance computations, which is a core mathematical operation that plays a critical role in a wide range of algorithms, particularly in machine learning and data analysis. The Euclidean distance, bei...
Gespeichert in:
| Veröffentlicht in: | Mathematics (Basel) Jg. 12; H. 23; S. 3787 |
|---|---|
| 1. Verfasser: | |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
Basel
MDPI AG
01.12.2024
|
| Schlagworte: | |
| ISSN: | 2227-7390, 2227-7390 |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| Abstract | This paper presents a comparative analysis of seventeen different approaches to optimizing Euclidean distance computations, which is a core mathematical operation that plays a critical role in a wide range of algorithms, particularly in machine learning and data analysis. The Euclidean distance, being a computational bottleneck in large-scale optimization problems, requires efficient computation techniques to improve the performance of various distance-dependent algorithms. To address this, several optimization strategies can be employed to accelerate distance computations. From spatial data structures and approximate nearest neighbor algorithms to dimensionality reduction, vectorization, and parallel computing, various approaches exist to accelerate Euclidean distance computation in different contexts. Such approaches are particularly important for speeding up key machine learning algorithms like K-means and K-nearest neighbors (KNNs). By understanding the trade-offs and assessing the effectiveness, complexity, and scalability of various optimization techniques, our findings help practitioners choose the most appropriate methods for improving Euclidean distance computations in specific contexts. These optimizations enable scalable and efficient processing for modern data-driven tasks, directly leading to reduced energy consumption and a minimized environmental impact. |
|---|---|
| AbstractList | This paper presents a comparative analysis of seventeen different approaches to optimizing Euclidean distance computations, which is a core mathematical operation that plays a critical role in a wide range of algorithms, particularly in machine learning and data analysis. The Euclidean distance, being a computational bottleneck in large-scale optimization problems, requires efficient computation techniques to improve the performance of various distance-dependent algorithms. To address this, several optimization strategies can be employed to accelerate distance computations. From spatial data structures and approximate nearest neighbor algorithms to dimensionality reduction, vectorization, and parallel computing, various approaches exist to accelerate Euclidean distance computation in different contexts. Such approaches are particularly important for speeding up key machine learning algorithms like K-means and K-nearest neighbors (KNNs). By understanding the trade-offs and assessing the effectiveness, complexity, and scalability of various optimization techniques, our findings help practitioners choose the most appropriate methods for improving Euclidean distance computations in specific contexts. These optimizations enable scalable and efficient processing for modern data-driven tasks, directly leading to reduced energy consumption and a minimized environmental impact. |
| Audience | Academic |
| Author | Mussabayev, Rustam |
| Author_xml | – sequence: 1 givenname: Rustam orcidid: 0000-0001-7283-5144 surname: Mussabayev fullname: Mussabayev, Rustam |
| BookMark | eNptUUtPwzAMjtCQGGM3fsAkTkh05NE2yXEa4yFN2gXOUZrHyNQ2JU0P8OsJFKEJYR9s2d_32bLPwaT1rQHgEsElIRzeNjK-IowJoYyegCnGmGY0NSZH-RmY9_0BJuOIsJxPwfWui65xH67dLzaDqp02sl3cuT7KVpnF2jfdEGV0vr0Ap1bWvZn_xBl4ud88rx-z7e7hab3aZiqHZcxKyXnFIWKa47K0tqCV0pWsrNaFQtwUsNJYV4QjKjFDplDMQqMZw1ZZbnIyA0-jrvbyILrgGhnehZdOfBd82AsZolO1EWVJYc6MktbwXKWJJeMUE6uUZQUmKGldjVpd8G-D6aM4-CG0aX1BUJ6jgjBGE2o5ovYyibrW-hikSq5N41S6snWpvmKI8wLyHCbCzUhQwfd9MPZ3TQTF1zPE8TMSHP-BKzfeNM1x9f-kT0H1jiQ |
| CitedBy_id | crossref_primary_10_1016_j_dcan_2025_06_011 crossref_primary_10_2478_amns_2025_0605 crossref_primary_10_1016_j_measurement_2025_118896 crossref_primary_10_1109_LCA_2025_3596970 crossref_primary_10_1016_j_eswa_2025_129642 crossref_primary_10_1002_qre_70075 crossref_primary_10_1007_s10462_025_11276_w crossref_primary_10_1016_j_procs_2025_07_222 |
| Cites_doi | 10.1109/TPDS.2014.2355205 10.1109/ICECCO.2015.7416879 10.1016/j.procs.2015.07.392 10.1109/ICECCO.2018.8634692 10.1109/TSP.2015.7296368 10.1162/tacl_a_00051 10.1137/1.9781611972801.12 10.1109/LES.2017.2764542 10.1007/3-540-44503-X_27 10.1145/361002.361007 10.1109/ACCESS.2023.3235207 10.1016/j.patcog.2022.109269 10.1007/3-540-49257-7_15 10.1007/978-981-97-4985-0 10.1145/502512.502546 10.1038/s41586-020-2649-2 10.1007/978-3-030-27656-0 10.1007/978-3-662-52844-0 10.1145/3447755 10.1016/0167-8655(92)90064-7 10.1016/j.patrec.2019.11.024 10.1145/276698.276876 10.1186/s12859-023-05311-2 10.1007/978-3-319-60792-4 10.1023/B:MACH.0000033118.09057.80 10.1145/2786984.2786995 10.1111/jscm.12073 10.1038/s41598-024-74061-9 10.14778/2732219.2732225 10.1201/9780203739051 10.1186/s13638-021-02082-3 10.1016/j.ins.2020.10.045 10.1145/2833157.2833162 10.1109/TITS.2022.3210170 10.1038/s41592-018-0019-x 10.1016/j.ins.2022.11.082 10.1007/978-3-031-05744-1 10.1109/97.863145 10.1038/s41598-022-10358-x 10.1016/j.eswa.2008.01.039 10.1109/TSC.2024.3478730 10.1016/j.patcog.2018.12.022 10.1016/j.patcog.2019.04.014 10.1145/1327452.1327492 10.1007/s10462-022-10325-y |
| ContentType | Journal Article |
| Copyright | COPYRIGHT 2024 MDPI AG 2024 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: COPYRIGHT 2024 MDPI AG – notice: 2024 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/math12233787 |
| 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 Advanced Technologies & Computer Science 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 Central Premium ProQuest One Academic (New) Publicly Available Content Database ProQuest One Academic Middle East (New) ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Applied & Life Sciences ProQuest One Academic (retired) ProQuest One Academic UKI Edition ProQuest Central China Engineering Collection 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 | CrossRef Publicly Available Content Database |
| 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: ProQuest Publicly Available Content Database url: http://search.proquest.com/publiccontent sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Mathematics |
| EISSN | 2227-7390 |
| ExternalDocumentID | oai_doaj_org_article_667048ecafe94c8d9689723fccf85231 A819950940 10_3390_math12233787 |
| GeographicLocations | Germany |
| GeographicLocations_xml | – name: Germany |
| GroupedDBID | -~X 5VS 85S 8FE 8FG AADQD AAFWJ AAYXX ABDBF ABJCF ABPPZ ABUWG ACIPV ACIWK ADBBV AFFHD AFKRA AFPKN AFZYC ALMA_UNASSIGNED_HOLDINGS AMVHM ARAPS AZQEC BCNDV BENPR BGLVJ BPHCQ CCPQU CITATION DWQXO GNUQQ GROUPED_DOAJ HCIFZ IAO ITC K6V K7- KQ8 L6V M7S MODMG M~E OK1 PHGZM PHGZT PIMPY PQGLB PQQKQ PROAC PTHSS RNS 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-c406t-6a99b9018d9266ff57bcdbabfdd5c19e50bd2db3917a281e5c8f0ed882fcf9e43 |
| IEDL.DBID | K7- |
| ISICitedReferencesCount | 12 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001376480700001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 2227-7390 |
| IngestDate | Mon Nov 10 04:30:56 EST 2025 Fri Jul 25 11:46:17 EDT 2025 Tue Nov 04 18:25:15 EST 2025 Sat Nov 29 07:12:04 EST 2025 Tue Nov 18 21:07:00 EST 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 23 |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c406t-6a99b9018d9266ff57bcdbabfdd5c19e50bd2db3917a281e5c8f0ed882fcf9e43 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| ORCID | 0000-0001-7283-5144 |
| OpenAccessLink | https://www.proquest.com/docview/3144153887?pq-origsite=%requestingapplication% |
| PQID | 3144153887 |
| PQPubID | 2032364 |
| ParticipantIDs | doaj_primary_oai_doaj_org_article_667048ecafe94c8d9689723fccf85231 proquest_journals_3144153887 gale_infotracacademiconefile_A819950940 crossref_primary_10_3390_math12233787 crossref_citationtrail_10_3390_math12233787 |
| PublicationCentury | 2000 |
| PublicationDate | 2024-12-01 |
| PublicationDateYYYYMMDD | 2024-12-01 |
| PublicationDate_xml | – month: 12 year: 2024 text: 2024-12-01 day: 01 |
| PublicationDecade | 2020 |
| PublicationPlace | Basel |
| PublicationPlace_xml | – name: Basel |
| PublicationTitle | Mathematics (Basel) |
| PublicationYear | 2024 |
| Publisher | MDPI AG |
| Publisher_xml | – name: MDPI AG |
| References | Hinton (ref_50) 2008; 9 Oyewole (ref_7) 2023; 56 Mussabayev (ref_26) 2023; 137 ref_13 ref_57 ref_56 ref_11 ref_55 ref_10 ref_51 Maitrey (ref_29) 2015; 57 Sieranoja (ref_38) 2019; 93 ref_19 Bock (ref_35) 2008; 4 Harris (ref_58) 2020; 585 ref_16 ref_59 Bentley (ref_33) 1975; 18 Jolliffe (ref_48) 2016; 374 ref_61 Ramasubramanian (ref_40) 1992; 13 Qi (ref_30) 2013; 7 Rodriguez (ref_53) 2018; 3 Lee (ref_54) 2017; 10 ref_21 ref_20 Bojanowski (ref_23) 2017; 5 Borodin (ref_52) 2004; 56 Ying (ref_36) 2012; 13 ref_28 ref_27 McNames (ref_46) 2000; 7 Tang (ref_12) 2020; 129 ref_34 Li (ref_24) 2021; 549 ref_32 ref_31 Varoquaux (ref_8) 2015; 19 ref_39 Shen (ref_41) 2023; 621 Gribel (ref_62) 2019; 88 ref_37 Rong (ref_17) 2022; 23 Altman (ref_25) 2018; 15 Dean (ref_60) 2008; 51 Mukhamediev (ref_22) 2023; 11 Jeon (ref_42) 2015; 26 ref_47 Sun (ref_15) 2024; 14 ref_45 ref_43 ref_1 ref_3 ref_2 ref_9 Wu (ref_18) 2022; 2022 ref_5 Carter (ref_14) 2015; 51 ref_4 Park (ref_44) 2009; 36 Gewers (ref_49) 2021; 54 ref_6 |
| References_xml | – volume: 26 start-page: 2534 year: 2015 ident: ref_42 article-title: Multi-Threaded Hierarchical Clustering by Parallel Nearest-Neighbor Chaining publication-title: IEEE Trans. Parallel Distrib. Syst. doi: 10.1109/TPDS.2014.2355205 – ident: ref_21 doi: 10.1109/ICECCO.2015.7416879 – volume: 57 start-page: 563 year: 2015 ident: ref_29 article-title: MapReduce: Simplified data analysis of big data publication-title: Procedia Comput. Sci. doi: 10.1016/j.procs.2015.07.392 – ident: ref_5 – ident: ref_32 – ident: ref_55 – ident: ref_10 doi: 10.1109/ICECCO.2018.8634692 – ident: ref_59 doi: 10.1109/TSP.2015.7296368 – volume: 4 start-page: 1 year: 2008 ident: ref_35 article-title: Origins and extensions of the k-means algorithm in cluster analysis publication-title: Electron. J. Hist. Probab. Stat. – volume: 5 start-page: 135 year: 2017 ident: ref_23 article-title: Enriching word vectors with subword information publication-title: Trans. Assoc. Comput. Linguist. doi: 10.1162/tacl_a_00051 – ident: ref_39 doi: 10.1137/1.9781611972801.12 – volume: 10 start-page: 18 year: 2017 ident: ref_54 article-title: Data-dependent loop approximations for performance-quality driven high-level synthesis publication-title: IEEE Embed. Syst. Lett. doi: 10.1109/LES.2017.2764542 – volume: 13 start-page: 1 year: 2012 ident: ref_36 article-title: Distance metric learning with eigenvalue optimization publication-title: J. Mach. Learn. Res. – ident: ref_28 doi: 10.1007/3-540-44503-X_27 – volume: 18 start-page: 509 year: 1975 ident: ref_33 article-title: Multidimensional binary search trees used for associative searching publication-title: Commun. ACM doi: 10.1145/361002.361007 – volume: 374 start-page: 20150202 year: 2016 ident: ref_48 article-title: Principal component analysis: A review and recent developments publication-title: Philos. Trans. R. Soc. Math. Phys. Eng. Sci. – ident: ref_61 – volume: 11 start-page: 5789 year: 2023 ident: ref_22 article-title: Coverage path planning optimization of heterogeneous UAVs group for precision agriculture publication-title: IEEE Access doi: 10.1109/ACCESS.2023.3235207 – volume: 137 start-page: 109269 year: 2023 ident: ref_26 article-title: How to use K-means for big data clustering? publication-title: Pattern Recognit. doi: 10.1016/j.patcog.2022.109269 – ident: ref_47 doi: 10.1007/3-540-49257-7_15 – ident: ref_3 doi: 10.1007/978-981-97-4985-0 – ident: ref_51 doi: 10.1145/502512.502546 – ident: ref_31 – volume: 585 start-page: 357 year: 2020 ident: ref_58 article-title: Array programming with NumPy publication-title: Nature doi: 10.1038/s41586-020-2649-2 – ident: ref_6 doi: 10.1007/978-3-030-27656-0 – ident: ref_1 doi: 10.1007/978-3-662-52844-0 – volume: 9 start-page: 2579 year: 2008 ident: ref_50 article-title: Visualizing Data using t-SNE publication-title: J. Mach. Learn. Res. – ident: ref_13 – volume: 54 start-page: 1 year: 2021 ident: ref_49 article-title: Principal component analysis: A natural approach to data exploration publication-title: ACM Comput. Surv. (CSUR) doi: 10.1145/3447755 – volume: 13 start-page: 471 year: 1992 ident: ref_40 article-title: An efficient approximation-elimination algorithm for fast nearest-neighbour search based on a spherical distance coordinate formulation publication-title: Pattern Recognit. Lett. doi: 10.1016/0167-8655(92)90064-7 – volume: 129 start-page: 123 year: 2020 ident: ref_12 article-title: Integrating prediction and reconstruction for anomaly detection publication-title: Pattern Recognit. Lett. doi: 10.1016/j.patrec.2019.11.024 – ident: ref_45 doi: 10.1145/276698.276876 – ident: ref_20 doi: 10.1186/s12859-023-05311-2 – ident: ref_4 doi: 10.1007/978-3-319-60792-4 – volume: 56 start-page: 153 year: 2004 ident: ref_52 article-title: Subquadratic approximation algorithms for clustering problems in high dimensional spaces publication-title: Mach. Learn. doi: 10.1023/B:MACH.0000033118.09057.80 – volume: 19 start-page: 29 year: 2015 ident: ref_8 article-title: Scikit-learn: Machine learning without learning the machinery publication-title: Getmobile Mob. Comput. Commun. doi: 10.1145/2786984.2786995 – ident: ref_34 – volume: 51 start-page: 89 year: 2015 ident: ref_14 article-title: Toward the theory of the supply chain publication-title: J. Supply Chain. Manag. doi: 10.1111/jscm.12073 – volume: 14 start-page: 1 year: 2024 ident: ref_15 article-title: Profit Maximization of Independent Task Offloading in MEC-Enabled 5G Internet of Vehicles publication-title: IEEE Trans. Intell. Transp. Syst. – ident: ref_37 – ident: ref_19 doi: 10.1038/s41598-024-74061-9 – volume: 7 start-page: 61 year: 2013 ident: ref_30 article-title: Toward a distance oracle for billion-node graphs publication-title: Proc. VLDB Endow. doi: 10.14778/2732219.2732225 – volume: 3 start-page: 1 year: 2018 ident: ref_53 article-title: Lower numerical precision deep learning inference and training publication-title: Intel White Pap. – ident: ref_11 doi: 10.1201/9780203739051 – volume: 2022 start-page: 4 year: 2022 ident: ref_18 article-title: Single base station hybrid TOA/AOD/AOA localization algorithms with the synchronization error in dense multipath environment publication-title: Eurasip J. Wirel. Commun. Netw. doi: 10.1186/s13638-021-02082-3 – volume: 549 start-page: 328 year: 2021 ident: ref_24 article-title: Random walk based distributed representation learning and prediction on social networking services publication-title: Inf. Sci. doi: 10.1016/j.ins.2020.10.045 – ident: ref_56 doi: 10.1145/2833157.2833162 – volume: 23 start-page: 24524 year: 2022 ident: ref_17 article-title: Du-Bus: A Realtime Bus Waiting Time Estimation System Based On Multi-Source Data publication-title: IEEE Trans. Intell. Transp. Syst. doi: 10.1109/TITS.2022.3210170 – ident: ref_2 – volume: 15 start-page: 399 year: 2018 ident: ref_25 article-title: The curse (s) of dimensionality publication-title: Nat. Methods doi: 10.1038/s41592-018-0019-x – volume: 621 start-page: 611 year: 2023 ident: ref_41 article-title: TC-DTW: Accelerating multivariate dynamic time warping through triangle inequality and point clustering publication-title: Inf. Sci. doi: 10.1016/j.ins.2022.11.082 – ident: ref_9 doi: 10.1007/978-3-031-05744-1 – volume: 7 start-page: 244 year: 2000 ident: ref_46 article-title: Rotated partial distance search for faster vector quantization encoding publication-title: IEEE Signal Process. Lett. doi: 10.1109/97.863145 – ident: ref_27 doi: 10.1038/s41598-022-10358-x – volume: 36 start-page: 3336 year: 2009 ident: ref_44 article-title: A simple and fast algorithm for K-medoids clustering publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2008.01.039 – ident: ref_16 doi: 10.1109/TSC.2024.3478730 – ident: ref_43 – ident: ref_57 – volume: 88 start-page: 569 year: 2019 ident: ref_62 article-title: HG-means: A scalable hybrid genetic algorithm for minimum sum-of-squares clustering publication-title: Pattern Recognit. doi: 10.1016/j.patcog.2018.12.022 – volume: 93 start-page: 95 year: 2019 ident: ref_38 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 – volume: 51 start-page: 107 year: 2008 ident: ref_60 article-title: MapReduce: Simplified data processing on large clusters publication-title: Commun. ACM doi: 10.1145/1327452.1327492 – volume: 56 start-page: 6439 year: 2023 ident: ref_7 article-title: Data clustering: Application and trends publication-title: Artif. Intell. Rev. doi: 10.1007/s10462-022-10325-y |
| SSID | ssj0000913849 |
| Score | 2.3548172 |
| Snippet | This paper presents a comparative analysis of seventeen different approaches to optimizing Euclidean distance computations, which is a core mathematical... |
| SourceID | doaj proquest gale crossref |
| SourceType | Open Website Aggregation Database Enrichment Source Index Database |
| StartPage | 3787 |
| SubjectTerms | Algorithms Bioinformatics Clustering Comparative analysis Computational linguistics computational optimization Data analysis Data mining Data structures Datasets Efficiency Energy conservation Energy consumption Euclidean distance Euclidean geometry Euclidean space Geospatial data Germany Information management K-means clustering K-nearest neighbors (KNNs) Language processing Linear algebra Machine learning Mathematical optimization Methods Natural language interfaces Optimization parallelization Spatial data Task complexity vectorization |
| SummonAdditionalLinks | – databaseName: DOAJ Directory of Open Access Journals dbid: DOA link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV09T8MwELVQxQAD4lMECsoAQghFJLGT2GOBViwUBpC6WfHZFpVKQP1g4NdzdtIqS8XCanmw39m-96TzO0IuClGkoHLUJiyjEYM0iXhiAc-yQfpaJiAM-GYTxXDIRyPx0mr15WrCanvgGjjXrQ8PmYHSGsGAa5Fz1yjLAliOIsoLH2Q9LTHl32CRUM5EXelOUdffIv97TzAX0sJVz7VykLfqX_cg-ywz2CU7DT0Me_Wy9siGqfbJ9tPKW3V2QK6f8ZZ_jH8w54T9BUzG2pRV-OB4IAYwrNs0eLwPydug_3r_GDUNDyLAvDqP8lIIhQkad4l509qsUKBVqazWGSTCZLHSqVYUJVaZ8sRkwG1sNJJkC1YYRo9Ip_qszDEJbcoMDqW51jlTiB3PmNI0NnFepgyKgNwsIZDQuIG7phQTiarAASbbgAXkcjX7q3bBWDPvzqG5muO8q_0ARlQ2EZV_RTQgVy4W0t0wXBKUzUcB3JjzqpI97r6VO9-_gHSX4ZLN1ZtJ6iUixcfz5D9Wc0q2UuQxdQVLl3Tm04U5I5vwPR_Ppuf-1P0CgBPb9A priority: 102 providerName: Directory of Open Access Journals |
| Title | Optimizing Euclidean Distance Computation |
| URI | https://www.proquest.com/docview/3144153887 https://doaj.org/article/667048ecafe94c8d9689723fccf85231 |
| Volume | 12 |
| WOSCitedRecordID | wos001376480700001&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: 2227-7390 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000913849 issn: 2227-7390 databaseCode: DOA dateStart: 20130101 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: 2227-7390 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000913849 issn: 2227-7390 databaseCode: M~E dateStart: 20130101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre – providerCode: PRVPQU databaseName: Computer Science Database customDbUrl: eissn: 2227-7390 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000913849 issn: 2227-7390 databaseCode: K7- dateStart: 20130301 isFulltext: true titleUrlDefault: http://search.proquest.com/compscijour providerName: ProQuest – providerCode: PRVPQU databaseName: Engineering Database customDbUrl: eissn: 2227-7390 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000913849 issn: 2227-7390 databaseCode: M7S dateStart: 20130301 isFulltext: true titleUrlDefault: http://search.proquest.com providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: eissn: 2227-7390 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000913849 issn: 2227-7390 databaseCode: BENPR dateStart: 20130301 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Publicly Available Content Database customDbUrl: eissn: 2227-7390 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000913849 issn: 2227-7390 databaseCode: PIMPY dateStart: 20130301 isFulltext: true titleUrlDefault: http://search.proquest.com/publiccontent providerName: ProQuest |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1LbxMxEB5BywEOLa-KlBLtAYQQWjVr78M-VS2kAqGGiIdUTtZ6bEOkkpQk7aEHfjszXif0Ui5c9mDPwfa8veNvAJ43uhFoa8pNykrmJYoiV0VAkmVP4WtboPYYm000o5E6PdXjdOG2SGWVK5sYDbWbId-R78sY-UvSiYPzXzl3jeK_q6mFxm3YLIQoWM4_NPn6joUxL1Wpu3p3Sdn9PkWBP4hSyoZr6K55ogjYf5NZjr7mePt_V3kftlKUmR12YvEAbvnpQ7h3soZoXTyCVx_JWPycXJHryoYXeDZxvp1mbzmcJDnIum4PkW2P4evx8Mubd3nqm5AjuedlXrdaW_LzymlyvyFUjUVnWxucq7DQvhpYJ5yVlKm1QhW-QhUG3lGsHTBoX8od2JjOpv4JZEGUnoZE7Vxd2uC1qkrr5MAP6laU2PTg9eoMDSZQce5tcWYoueATN9dPvAcv1tTnHZjGDXRHzI41DUNgx4HZ_LtJGmXquiHr47GlVZVIe60Vd1ALiEFRdl304CUz07Ci0pKwTe8NaGMMeWUOFb9OZ_jAHuytmGmSBi_MX07u_nv6KdwVFOh0JS57sLGcX_hncAcvl5PFvA-bR8PR-FM_5vr9KJ59ri_9zN_fQ5ofvz8Zf_sDY1vwPA |
| linkProvider | ProQuest |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Nb9QwEB2VggQc-EYsFMiBCiEUNbGdxD4gVGirVtsuHIrUmxuPbVip7JbdLQh-FL-RmXwsvZRbD1wdK7IzzzNjZ_wewIvKVAJdSXsTVchUochTnUckLAdKX-scTcBGbKIajfTRkfm4Ar_7uzBcVtn7xMZR-ynyGfmGbDJ_SWvi7em3lFWj-O9qL6HRwmIYfv6gLdv8zd4W2XddiJ3tw_e7aacqkCIFr0Va1sY4ioLaGwpOMRaVQ-9qF70vMDehyJwX3knax9RC56FAHbPgKRONGE1Qkt57Ba4qqSvm6h9W6fJMhzk2tTJtfb2UJtugrPNLThFYVlyzdy7yNQIBF4WBJrbt3P7fvsoduNVl0clmC_u7sBIm9-DmwZKCdn4fXn0gZ_h1_ItCc7J9hidjH-pJssXpMuE8adUsGlg-gE-XMtaHsDqZTsIjSKJQgZpE6X2pXAxGF8p5mYWsrIXCagCve5tZ7EjTWbvjxNLmiS1sz1t4AOvL3qctWcgF_d6x-Zd9mOK7aZjOPtvOY9iyrMi7BqxpVApprqVmhbiIGHVBWfkAXjJ4LDsiGhLW3X0KmhhTetlNzbfvmR5xAGs9eGznoeb2L3Ie__vxc7i-e3iwb_f3RsMncENQUteW86zB6mJ2Fp7CNfy-GM9nz5rFkMDxZePsD3BZSi0 |
| linkToPdf | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1LbxMxEB6VglB74F0RKLAHKoTQKrv2PuwDQoU0ogqEHkDqzazHdhupJG2SguCn8euY2Ufopdx64Oq1VvbON_PNeMczAM9LXQq0BcUmWS7jDEUaqzQgYdmT-1qlqD3WzSbK8VgdHuqDNfjd3YXhtMrOJtaG2s2Qz8j7svb8JelEP7RpEQeD4ZvTs5g7SPGf1q6dRgORkf_5g8K3xev9Acl6R4jh3ud37-O2w0CMRGTLuKi0tsSIymkiqhDy0qKzlQ3O5ZhqnyfWCWclxTSVUKnPUYXEO_JKAwbtM0nvvQbXiYVz1rFRGa_Od7jepsp0k2svpU765IEep8TGsuT8vQssWDcLuIwSap4b3v6fv9AduNV619Fuow53Yc1P78Hmx1Vp2sV9ePmJjOS3yS-i7GjvHE8mzlfTaMBuNOE_arpc1HB9AF-uZK1bsD6dTf1DiILIPA2Jwrkis8FrlWfWycQnRSUyLHvwqpOfwbaYOvf0ODEUVLG0zUVp92BnNfu0KSJyyby3DIXVHC79XQ_M5kemtSSmKEqyuh4rWlWGtNdCcee4gBhUTt56D14wkAwbKFoSVu09C9oYl_oyu4pv5XPZxB5sd0AyreVamL8oevTvx8_gJsHLfNgfjx7DhiBfr8ny2Yb15fzcP4Eb-H05Wcyf1noRwderhtkfLCtS5w |
| 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=Optimizing+Euclidean+Distance+Computation&rft.jtitle=Mathematics+%28Basel%29&rft.au=Mussabayev%2C+Rustam&rft.date=2024-12-01&rft.pub=MDPI+AG&rft.issn=2227-7390&rft.eissn=2227-7390&rft.volume=12&rft.issue=23&rft_id=info:doi/10.3390%2Fmath12233787&rft.externalDocID=A819950940 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2227-7390&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2227-7390&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2227-7390&client=summon |