Article

Streaming-Data Algorithms for High-Quality Clustering

ICDE '02: Proceedings of the 18th International Conference on Data Engineering

Page 685

Published: 26 February 2002 Publication History

Abstract

Streaming data analysis has recently attracted attention in numerous applications including telephone records, web documents and clickstreams. For such analysis, single-pass algorithms that consume a small amount of memory are critical. We describe such a streaming algorithm that effectively clusters large data streams. We also provide empirical evidence of the algorithm's performance on synthetic and real data streams.

Cited By

View all

Qin KQin Y(2019)Hierarchical Cluster-Based Adaptive Model for Semi-Supervised Classification of Data Stream with Concept DriftProceedings of the 2019 International Conference on Artificial Intelligence and Computer Science10.1145/3349341.3349366(41-49)Online publication date: 12-Jul-2019
https://dl.acm.org/doi/10.1145/3349341.3349366
Dong SLiu SZhao YShao Z(2019)An innovative model to mine asynchronous periodic pattern of moving objectsMultimedia Tools and Applications10.1007/s11042-018-6752-478:7(8943-8964)Online publication date: 1-Apr-2019
https://dl.acm.org/doi/10.1007/s11042-018-6752-4
Chen JLin XXuan QXiang Y(2019)FGCHApplied Intelligence10.1007/s10489-018-1324-x49:4(1228-1244)Online publication date: 1-Apr-2019
https://dl.acm.org/doi/10.1007/s10489-018-1324-x
Show More Cited By

Streaming-Data Algorithms for High-Quality Clustering
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
  2. Information systems applications
    1. Data mining
2. Mathematics of computing
  1. Mathematical analysis
    1. Functional analysis

Recommendations

Research on data stream clustering algorithms

Data stream is a potentially massive, continuous, rapid sequence of data information. It has aroused great concern and research upsurge in the field of data mining. Clustering is an effective tool of data mining, so data stream clustering will ...
Density-based hierarchical clustering for streaming data

For streaming data that arrive continuously such as multimedia data and financial transactions, clustering algorithms are typically allowed to scan the data set only once. Existing research in this domain mainly focuses on improving the accuracy of ...
Clustering data stream: A survey of algorithms

A data stream is a massive, continuous and rapid sequence of data elements. The data stream model requires algorithms to make a single pass over the data, with bounded memory and limited processing time, whereas the stream may be highly dynamic and ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

ICDE '02: Proceedings of the 18th International Conference on Data Engineering

February 2002

Publisher

IEEE Computer Society

United States

Publication History

Published: 26 February 2002

Qualifiers

Article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

81
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 18 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

View all

Qin KQin Y(2019)Hierarchical Cluster-Based Adaptive Model for Semi-Supervised Classification of Data Stream with Concept DriftProceedings of the 2019 International Conference on Artificial Intelligence and Computer Science10.1145/3349341.3349366(41-49)Online publication date: 12-Jul-2019
https://dl.acm.org/doi/10.1145/3349341.3349366
Dong SLiu SZhao YShao Z(2019)An innovative model to mine asynchronous periodic pattern of moving objectsMultimedia Tools and Applications10.1007/s11042-018-6752-478:7(8943-8964)Online publication date: 1-Apr-2019
https://dl.acm.org/doi/10.1007/s11042-018-6752-4
Chen JLin XXuan QXiang Y(2019)FGCHApplied Intelligence10.1007/s10489-018-1324-x49:4(1228-1244)Online publication date: 1-Apr-2019
https://dl.acm.org/doi/10.1007/s10489-018-1324-x
Wang ZFarhand STsechpenakis G(2019)Fading affect biasMachine Vision and Applications10.1007/s00138-019-01008-w30:2(255-268)Online publication date: 1-Mar-2019
https://dl.acm.org/doi/10.1007/s00138-019-01008-w
Wang XWang LEmrouznejad AQian Z(2018)Research on Data Stream Clustering Algorithm Based on Decay Time WindowProceedings of the 2nd International Conference on Computer Science and Application Engineering10.1145/3207677.3277972(1-7)Online publication date: 22-Oct-2018
https://dl.acm.org/doi/10.1145/3207677.3277972
Pandove DGoel SRani R(2018)Systematic Review of Clustering High-Dimensional and Large DatasetsACM Transactions on Knowledge Discovery from Data10.1145/313208812:2(1-68)Online publication date: 23-Jan-2018
https://dl.acm.org/doi/10.1145/3132088
Liu ZZheng QJi ZZhao W(2018)Sparse Self-Represented Network MapEngineering Applications of Artificial Intelligence10.1016/j.engappai.2017.11.00268:C(121-130)Online publication date: 1-Feb-2018
https://dl.acm.org/doi/10.1016/j.engappai.2017.11.002
Carnein MAssenmacher DTrautmann HGiorgi RBecchi MPalumbo F(2017)An Empirical Comparison of Stream Clustering AlgorithmsProceedings of the Computing Frontiers Conference10.1145/3075564.3078887(361-366)Online publication date: 15-May-2017
https://dl.acm.org/doi/10.1145/3075564.3078887
Callister RLazarescu MPham DShin SShin DLencastre M(2017)Graph-based clustering with DRepStreamProceedings of the Symposium on Applied Computing10.1145/3019612.3019672(850-857)Online publication date: 3-Apr-2017
https://dl.acm.org/doi/10.1145/3019612.3019672
Silva JHruschka E(2016)A Support System for Clustering Data Streams with a Variable Number of ClustersACM Transactions on Autonomous and Adaptive Systems10.1145/293270411:2(1-26)Online publication date: 25-Jul-2016
https://dl.acm.org/doi/10.1145/2932704
Show More Cited By

Abstract

Cited By

Recommendations

Research on data stream clustering algorithms

Density-based hierarchical clustering for streaming data

Clustering data stream: A survey of algorithms

Comments

Information

Published In

Publisher

Publication History

Qualifiers

Contributors

Other Metrics

Bibliometrics

Article Metrics

Other Metrics

Citations

Cited By

View options

Figures

Other

Share

Share this Publication link

Share on social media