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Evaluating a class of distance-mapping algorithms for data mining and clustering

Authors:

Kaizhong ZhangAuthors Info & Claims

KDD '99: Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining

Pages 307 - 311

https://doi.org/10.1145/312129.312264

Published: 01 August 1999 Publication History

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Cited By

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Delicado PPachón-García C(2024)Multidimensional scaling for big dataAdvances in Data Analysis and Classification10.1007/s11634-024-00591-9Online publication date: 13-Apr-2024
https://doi.org/10.1007/s11634-024-00591-9
Zymbler MKraeva Y(2023)High-Performance Time Series Anomaly Discovery on Graphics ProcessorsMathematics10.3390/math1114319311:14(3193)Online publication date: 20-Jul-2023
https://doi.org/10.3390/math11143193
Nakamura TMercer RImamura MKeogh E(2023)MERLIN++: parameter-free discovery of time series anomaliesData Mining and Knowledge Discovery10.1007/s10618-022-00876-737:2(670-709)Online publication date: 16-Jan-2023
https://doi.org/10.1007/s10618-022-00876-7
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Index Terms

Evaluating a class of distance-mapping algorithms for data mining and clustering
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Clustering and classification
  2. Information systems applications
    1. Data mining
      1. Clustering

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Published In

KDD '99: Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining

August 1999

439 pages

ISBN:1581131437

DOI:10.1145/312129

Chairmen:
Usama Fayyad
Microsoft Research
,
Surajit Chaudhuri
Microsoft Research
,
David Madigan
AT&T Labs-Research

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 August 1999

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KDD99

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KDD99: The First Annual International Conference on Knowledge Discovery in Data

August 15 - 18, 1999

California, San Diego, USA

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Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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Cited By

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Delicado PPachón-García C(2024)Multidimensional scaling for big dataAdvances in Data Analysis and Classification10.1007/s11634-024-00591-9Online publication date: 13-Apr-2024
https://doi.org/10.1007/s11634-024-00591-9
Zymbler MKraeva Y(2023)High-Performance Time Series Anomaly Discovery on Graphics ProcessorsMathematics10.3390/math1114319311:14(3193)Online publication date: 20-Jul-2023
https://doi.org/10.3390/math11143193
Nakamura TMercer RImamura MKeogh E(2023)MERLIN++: parameter-free discovery of time series anomaliesData Mining and Knowledge Discovery10.1007/s10618-022-00876-737:2(670-709)Online publication date: 16-Jan-2023
https://doi.org/10.1007/s10618-022-00876-7
Grané ASow-Barry A(2021)Visualizing Profiles of Large Datasets of Weighted and Mixed DataMathematics10.3390/math90808919:8(891)Online publication date: 16-Apr-2021
https://doi.org/10.3390/math9080891
Herath SRoughan MGlonek G(2021)Generating Name-Like Vectors for Testing Large-Scale Entity ResolutionIEEE Access10.1109/ACCESS.2021.31224519(145288-145300)Online publication date: 2021
https://doi.org/10.1109/ACCESS.2021.3122451
O’Hare KJurek-Loughrey ACampos C(2018)A Review of Unsupervised and Semi-supervised Blocking Methods for Record LinkageLinking and Mining Heterogeneous and Multi-view Data10.1007/978-3-030-01872-6_4(79-105)Online publication date: 27-Nov-2018
https://doi.org/10.1007/978-3-030-01872-6_4
Aved ABlasch E(2016)Context Understanding from Query-Based Streaming VideoContext-Enhanced Information Fusion10.1007/978-3-319-28971-7_19(507-537)Online publication date: 26-May-2016
https://doi.org/10.1007/978-3-319-28971-7_19
Mu Li Wei Bi Kwok JBao-Liang Lu (2015)Large-Scale Nyström Kernel Matrix Approximation Using Randomized SVDIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2014.235979826:1(152-164)Online publication date: Jan-2015
https://doi.org/10.1109/TNNLS.2014.2359798
Peng YFan ZChoi BXu JBhowmick S(2015)Authenticated Subgraph Similarity Searchin Outsourced Graph DatabasesIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2014.231681827:7(1838-1860)Online publication date: 1-Jul-2015
https://doi.org/10.1109/TKDE.2014.2316818
Alavi ZSharma SZhou LChen K(2015)Scalable Euclidean Embedding for Big DataProceedings of the 2015 IEEE 8th International Conference on Cloud Computing10.1109/CLOUD.2015.107(773-780)Online publication date: 27-Jun-2015
https://dl.acm.org/doi/10.1109/CLOUD.2015.107
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