research-article

A method for clustering transient data streams

Authors:

Pimwadee Chaovalit,

Aryya GangopadhyayAuthors Info & Claims

SAC '09: Proceedings of the 2009 ACM symposium on Applied Computing

Pages 1518 - 1519

https://doi.org/10.1145/1529282.1529622

Published: 08 March 2009 Publication History

Get Access

Abstract

This paper describes a novel method for clustering single and multi-dimensional data streams. With incremental computation of the incoming data, our method determines if the cluster formation should change from an initial cluster formation. Four main types of cluster evolutions are studied: cluster appearance, cluster disappearance, cluster splitting, and cluster merging. We present experimental results of our algorithms both in terms of scalability and cluster quality, compared with recent work in this area.

References

[1]

Aggarwal, C. C., Han, J., Wang, J., and Yu, P. S., A framework for projected clustering of high dimensional data streams, In Proceedings of the 30th International Conference on Very Large Data Bases (Toronto, Canada). 852--863.

Digital Library

Google Scholar

[2]

Guha, S., Meyerson, A., Mishra, N., Motwani, R., and O'Callaghan, L., Clustering data streams: Theory and practice IEEE Transactions on Knowledge and Data Engineering, 15, 515--528.

Digital Library

Google Scholar

[3]

Rodrigues, P. P., Gama, J., and Pedroso, J. P., Hierarchical clustering of time-series data streams IEEE Transactions on Knowledge and Data Engineering, 20, 615--627.

Digital Library

Google Scholar

[4]

Udommanetanakit, K., Rakthanmanon, T., and Waiyamai, K., E-Stream: evolution-based technique for stream clustering, In Proceedings of the 3rd International Conference on Advanced Data Mining and Applications (Harbin, China, August 6--8, 2007). Springer Berlin / Heidelberg, 605--615.

Digital Library

Google Scholar

Cited By

View all

Sousa Lima Ade Sousa E(2024)CETra: online cluster tracking for clustering of streaming data sourcesKnowledge and Information Systems10.1007/s10115-024-02267-467:2(1455-1479)Online publication date: 2-Nov-2024
https://doi.org/10.1007/s10115-024-02267-4
ŞENOL AKARACAN H(2019)K-boyutlu ağaç ve uyarlanabilir yarıçap (KD-AR Stream) tabanlı gerçek zamanlı akan veri kümelemeKd-tree and adaptive radius (KD-AR Stream) based real-time data stream clusteringGazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi10.17341/gazimmfd.46722635:1(337-354)Online publication date: 25-Oct-2019
https://doi.org/10.17341/gazimmfd.467226
ŞENOL AKARACAN H(2018)Akan Veri Kümeleme Teknikleri Üzerine Bir DerlemeEuropean Journal of Science and Technology10.31590/ejosat.446019(17-30)Online publication date: 31-Aug-2018
https://doi.org/10.31590/ejosat.446019
Show More Cited By

Index Terms

A method for clustering transient data streams
1. Information systems
  1. Information systems applications
    1. Data mining
2. Mathematics of computing
  1. Probability and statistics

Recommendations

An evolutionary algorithm for clustering data streams with a variable number of clusters

An evolutionary algorithm for clustering data stream is proposed.Our algorithm allows estimating k automatically from the data in an online fashion.It monitors eventual degradation in the quality of the induced clusters.Results show our algorithm ...
Improving Multivariate Data Streams Clustering

Clustering data streams is an important task in data mining research. Recently, some algorithms have been proposed to cluster data streams as a whole, but just few of them deal with multivariate data streams. Even so, these algorithms merely aggregate ...
Improved k- means clustering algorithm for two dimensional data
CCSEIT '12: Proceedings of the Second International Conference on Computational Science, Engineering and Information Technology

Clustering is a procedure of organizing the objects in groups whose member exhibits some kind of similarity. So a cluster is a collection of objects which are alike and are different from the objects belonging to other clusters. K-Means is one of ...

Comments

Information & Contributors

Information

Published In

SAC '09: Proceedings of the 2009 ACM symposium on Applied Computing

March 2009

2347 pages

ISBN:9781605581668

DOI:10.1145/1529282

Conference Chairs:
Sung Y. Shin
South Dakota State University, United States
,
Sascha Ossowski
University Rey Juan Carlos, Spain

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]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 08 March 2009

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

SAC09

Sponsor:

SIGAPP

SAC09: The 2009 ACM Symposium on Applied Computing

March 8, 2009 - March 12, 2008

Hawaii, Honolulu

Acceptance Rates

Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

Upcoming Conference

SAC '25

Sponsor:
sigapp

The 40th ACM/SIGAPP Symposium on Applied Computing

March 31 - April 4, 2025

Catania , Italy

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

6
Total Citations
View Citations
260
Total Downloads

Downloads (Last 12 months)1
Downloads (Last 6 weeks)0

Reflects downloads up to 08 Mar 2025

Other Metrics

View Author Metrics

Citations

Cited By

View all

Sousa Lima Ade Sousa E(2024)CETra: online cluster tracking for clustering of streaming data sourcesKnowledge and Information Systems10.1007/s10115-024-02267-467:2(1455-1479)Online publication date: 2-Nov-2024
https://doi.org/10.1007/s10115-024-02267-4
ŞENOL AKARACAN H(2019)K-boyutlu ağaç ve uyarlanabilir yarıçap (KD-AR Stream) tabanlı gerçek zamanlı akan veri kümelemeKd-tree and adaptive radius (KD-AR Stream) based real-time data stream clusteringGazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi10.17341/gazimmfd.46722635:1(337-354)Online publication date: 25-Oct-2019
https://doi.org/10.17341/gazimmfd.467226
ŞENOL AKARACAN H(2018)Akan Veri Kümeleme Teknikleri Üzerine Bir DerlemeEuropean Journal of Science and Technology10.31590/ejosat.446019(17-30)Online publication date: 31-Aug-2018
https://doi.org/10.31590/ejosat.446019
Bones CRomani Lde Sousa E(2016)Improving Multivariate Data Streams ClusteringProcedia Computer Science10.1016/j.procs.2016.05.32580:C(461-471)Online publication date: 1-Jun-2016
https://dl.acm.org/doi/10.1016/j.procs.2016.05.325
Yogita YToshniwal D(2013)Clustering techniques for streaming data-a survey2013 3rd IEEE International Advance Computing Conference (IACC)10.1109/IAdCC.2013.6514355(951-956)Online publication date: Feb-2013
https://doi.org/10.1109/IAdCC.2013.6514355
Chaovalit PGangopadhyay AKarabatis GChen Z(2011)Discrete wavelet transform-based time series analysis and miningACM Computing Surveys10.1145/1883612.188361343:2(1-37)Online publication date: 4-Feb-2011
https://dl.acm.org/doi/10.1145/1883612.1883613

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Abstract

References

Cited By

Index Terms

Recommendations

An evolutionary algorithm for clustering data streams with a variable number of clusters

Improving Multivariate Data Streams Clustering

Improved k- means clustering algorithm for two dimensional data

Comments

Information

Published In

Sponsors

Publisher

Publication History

Permissions

Check for updates

Author Tags

Qualifiers

Conference

Acceptance Rates

Upcoming Conference

Contributors

Other Metrics

Bibliometrics

Article Metrics

Other Metrics

Citations

Cited By

Login options

Full Access

View options

PDF

eReader

Share

Share this Publication link

Share on social media

Affiliations