More Web Proxy on the site http://driver.im/

article

FINGERPRINT: Summarizing Cluster Evolution in Dynamic Environments

Authors:

Yannis Theodoridis,

Eirini Ntoutsi,

Myra SpiliopoulouAuthors Info & Claims

International Journal of Data Warehousing and Mining, Volume 8, Issue 3

Pages 27 - 44

https://doi.org/10.4018/jdwm.2012070102

Published: 01 July 2012 Publication History

Abstract

Monitoring and interpretation of changing patterns is a task of paramount importance for data mining applications in dynamic environments. While there is much research in adapting patterns in the presence of drift or shift, there is less research on how to maintain an overview of pattern changes over time. A major challenge is summarizing changes in an effective way, so that the nature of change can be understood by the user, while the demand on resources remains low. To this end, the authors propose FINGERPRINT, an environment for the summarization of cluster evolution. Cluster changes are captured into an "evolution graph," which is then summarized based on cluster similarity into a fingerprint of evolution by merging similar clusters. The authors propose a batch summarization method that traverses and summarizes the Evolution Graph as a whole and an incremental method that is applied during the process of cluster transition discovery. They present experiments on different data streams and discuss the space reduction and information preservation achieved by the two methods.

References

[1]

Aggarwal, C. C. 2005. On change diagnosis in evolving data streams. IEEE Transactions on Knowledge and Data Engineering, 175, 587-600.

Digital Library

[2]

Aggarwal, C. C., Han, J., Wang, J.,&Yu, P. 2003. A framework for clustering evolving data streams. In Proceedings of the 29th International Conference on Very Large Data Bases Vol. 29, pp. 81-92. New York, NY: ACM.

[3]

Aggarwal, C. C.,&Yu, P. 2006. A framework for clustering massive text and categorical data streams. In Proceedings of the Sixth SIAM International Conference on Data Mining Vol. 124, pp. 479-483. Philadelphia, PA: Society for Industrial Mathematics.

[4]

Al-Mulla, R.,&Al Aghbari, Z. 2011. Incremental algorithm for discovering frequent subsequences in multiple data streams. International Journal of Data Warehousing and Mining, 74, 1-20.

Digital Library

[5]

Bartolini, I., Ciaccia, P., Ntoutsi, I., Patella, M.,&Theodoridis, Y. 2004. A unified and flexible framework for comparing simple and complex patterns. In Proceedings of the 8th European Conference on Principles and Practice of Knowledge Discovery in Databases pp. 496-499. New York, NY: Springer.

[6]

Cao, F., Ester, M., Qian, W.,&Zhou, A. 2006. Density-based clustering over an evolving data stream with noise. In Proceedings of the Sixth SIAM International Conference on Data Mining Vol. 124, pp. 479-483. Philadelphia, PA: Society for Industrial Mathematics.

[7]

Chandola, V.,&Kumar, V. 2005. Summarization - compressing data into an informative representation. In Proceedings of the Fifth IEEE International Conference on Data Mining pp. 98-105. Washington, DC: IEEE Computer Society.

Digital Library

[8]

Chen, Y.,&Tu, L. 2007. Density-based clustering for real-time stream data. In Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining pp. 133-142. New York, NY: ACM.

Digital Library

[9]

Farnstrom, F., Lewis, J.,&Elkan, C. 2000. Scalability for clustering algorithms revisited. SIGKDD Explorations, 21, 51-57.

Digital Library

[10]

Gama, J. 2010. Knowledge discovery from data streams. Boca Raton, FL: CRC Press.

[11]

Ipeirotis, P., Ntoulas, A.,&Gravano, L. 2005. Modeling and managing content changes in text databases. In Proceedings of the 21st International Conference on Data Engineering pp. 606-617. Washington, DC: IEEE Computer Society.

Digital Library

[12]

Kalnis, P., Mamoulis, N.,&Bakiras, S. 2005. On discovering moving clusters in spatio-temporal data. In Proceedings of the 9th International Conference on Advances in Spatial and Temporal Databases pp. 364-381. Berlin, Germany: Springer-Verlag.

[13]

Mei, Q.,&Zhai, C. 2005. Discovering evolutionary theme patterns from text: An exploration of temporal text mining. In Proceedings of the 11th ACM SIGKDD International Conference on Knowledge Discovery in Data Mining pp. 198-207. New York, NY: ACM.

[14]

Spiliopoulou, M. 2011. Evolution in social networks: A survey. In Aggarwal, C. Ed., Social network data analytics pp. 147-173. Boston, MA: Kluwer Academic.

[15]

Spiliopoulou, M., Ntoutsi, I., Theodoridis, Y.,&Schult, R. 2006. MONIC: Modeling and monitoring cluster transitions. In Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery in Data Mining pp. 706-711. New York, NY: ACM.

[16]

Yang, H., Parthasarathy, S.,&Mehta, S. 2005. A generalized framework for mining spatio-temporal patterns in scientific data. In Proceedings of the 11th ACM SIGKDD International Conference on Knowledge Discovery in Data Mining pp. 716-721. New York, NY: ACM.

[17]

Zhang, T., Ramakrishnan, R.,&Livny, M. 1996. BIRCH: An efficient data clustering method for very large databases. In Proceedings of the 11th ACM SIGKDD International Conference on Knowledge Discovery in Data Mining pp. 103-114. New York, NY: ACM.

Cited By

Sousa Lima Ade Sousa E(2025)CETra: online cluster tracking for clustering of streaming data sourcesKnowledge and Information Systems10.1007/s10115-024-02267-467:2(1455-1479)Online publication date: 1-Feb-2025
https://dl.acm.org/doi/10.1007/s10115-024-02267-4
Tritsarolis AChondrodima ETampakis PPikrakis ATheodoridis Y(2024)Predicting Co-movement patterns in mobility dataGeoinformatica10.1007/s10707-022-00478-x28:2(221-243)Online publication date: 1-Apr-2024
https://dl.acm.org/doi/10.1007/s10707-022-00478-x
Tritsarolis AKontoulis YPelekis NTheodoridis Y(2021)MaSEC: Discovering Anchorages and Co-movement Patterns on Streaming Vessel TrajectoriesProceedings of the 17th International Symposium on Spatial and Temporal Databases10.1145/3469830.3470909(170-173)Online publication date: 23-Aug-2021
https://dl.acm.org/doi/10.1145/3469830.3470909
Show More Cited By

FINGERPRINT: Summarizing Cluster Evolution in Dynamic Environments
1. Information systems
  1. Information systems applications

Recommendations

Arbitrary Shape Cluster Summarization with Gaussian Mixture Model
IC3K 2014: Proceedings of the International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1

One of the main concerns in the area of arbitrary shape clustering is how to summarize clusters. An accurate representation of clusters with arbitrary shapes is to characterize a cluster with all its members. However, this approach is neither practical ...
Discovering cluster evolution patterns with the Cluster Association-aware matrix factorization
Abstract
Tracking of document collections over time (or across domains) is helpful in several applications such as finding dynamics of terminologies, identifying emerging and evolving trends, and concept drift detection. We propose a novel ‘Cluster ...
Nesting the earth mover's distance for effective cluster tracing
SSDBM '13: Proceedings of the 25th International Conference on Scientific and Statistical Database Management

Cluster tracing algorithms are used to mine temporal evolutions of clusters. Generally, clusters represent groups of objects with similar values. In a temporal context like tracing, similar values correspond to similar behavior in one snapshot in time. ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image International Journal of Data Warehousing and Mining

International Journal of Data Warehousing and Mining Volume 8, Issue 3

July 2012

61 pages

ISSN:1548-3924

EISSN:1548-3932

Issue’s Table of Contents

Publisher

IGI Global

United States

Publication History

Published: 01 July 2012

Author Tags

Qualifiers

Article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

4
Total Citations
View Citations
0
Total Downloads

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

Reflects downloads up to 03 Mar 2025

Other Metrics

View Author Metrics

Citations

Cited By

Sousa Lima Ade Sousa E(2025)CETra: online cluster tracking for clustering of streaming data sourcesKnowledge and Information Systems10.1007/s10115-024-02267-467:2(1455-1479)Online publication date: 1-Feb-2025
https://dl.acm.org/doi/10.1007/s10115-024-02267-4
Tritsarolis AChondrodima ETampakis PPikrakis ATheodoridis Y(2024)Predicting Co-movement patterns in mobility dataGeoinformatica10.1007/s10707-022-00478-x28:2(221-243)Online publication date: 1-Apr-2024
https://dl.acm.org/doi/10.1007/s10707-022-00478-x
Tritsarolis AKontoulis YPelekis NTheodoridis Y(2021)MaSEC: Discovering Anchorages and Co-movement Patterns on Streaming Vessel TrajectoriesProceedings of the 17th International Symposium on Spatial and Temporal Databases10.1145/3469830.3470909(170-173)Online publication date: 23-Aug-2021
https://dl.acm.org/doi/10.1145/3469830.3470909
Zimmermann MNtoutsi ESpiliopoulou M(2015)Discovering and monitoring product features and the opinions on them with OPINSTREAMNeurocomputing10.1016/j.neucom.2014.04.079150:PA(318-330)Online publication date: 20-Feb-2015
https://dl.acm.org/doi/10.1016/j.neucom.2014.04.079

View Options

View options

Figures

Tables

Media

View Issue’s Table of Contents