research-article

Towards subspace clustering on dynamic data: an incremental version of PreDeCon

Authors:

Hans-Peter Kriegel,

Arthur ZimekAuthors Info & Claims

StreamKDD '10: Proceedings of the First International Workshop on Novel Data Stream Pattern Mining Techniques

Pages 31 - 38

https://doi.org/10.1145/1833280.1833285

Published: 25 July 2010 Publication History

Abstract

Todays data are high dimensional and dynamic, thus clustering over such kind of data is rather complicated. To deal with the high dimensionality problem, the subspace clustering research area has lately emerged that aims at finding clusters in subspaces of the original feature space. So far, the subspace clustering methods are mainly static and thus, cannot address the dynamic nature of modern data. In this paper, we propose an incremental version of the density based projected clustering algorithm PreDeCon, called incPreDeCon. The proposed algorithm efficiently updates only those subspace clusters that might be affected due to the population update.

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Cited By

Aggarwal C(2023)Clustering in StreamsMachine Learning for Data Science Handbook10.1007/978-3-031-24628-9_13(271-300)Online publication date: 26-Feb-2023
https://doi.org/10.1007/978-3-031-24628-9_13
Sui JLiu ZLiu LJung ALi X(2022)Dynamic Sparse Subspace Clustering for Evolving High-Dimensional Data StreamsIEEE Transactions on Cybernetics10.1109/TCYB.2020.302397352:6(4173-4186)Online publication date: Jun-2022
https://doi.org/10.1109/TCYB.2020.3023973
Borutta FKazempour DMathy FKröger PSeidl T(2020)Detecting Arbitrarily Oriented Subspace Clusters in Data Streams Using Hough TransformAdvances in Knowledge Discovery and Data Mining10.1007/978-3-030-47426-3_28(356-368)Online publication date: 6-May-2020
https://doi.org/10.1007/978-3-030-47426-3_28
Show More Cited By

Index Terms

Towards subspace clustering on dynamic data: an incremental version of PreDeCon
1. Information systems
  1. Information systems applications
    1. Data mining

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Published In

cover image ACM Conferences

StreamKDD '10: Proceedings of the First International Workshop on Novel Data Stream Pattern Mining Techniques

July 2010

66 pages

ISBN:9781450302265

DOI:10.1145/1833280

Conference Chairs:
Margaret H. Dunham
Southern Methodist University
,
Michael Hahsler
Southern Methodist University
,
Myra Spiliopoulou
Otto-von-Guericke-Universität Magdeburg, Germany

Copyright © 2010 ACM.

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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Publication History

Published: 25 July 2010

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KDD '10

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KDD '10: The 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

July 25, 2010

Washington, D.C.

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Cited By

Aggarwal C(2023)Clustering in StreamsMachine Learning for Data Science Handbook10.1007/978-3-031-24628-9_13(271-300)Online publication date: 26-Feb-2023
https://doi.org/10.1007/978-3-031-24628-9_13
Sui JLiu ZLiu LJung ALi X(2022)Dynamic Sparse Subspace Clustering for Evolving High-Dimensional Data StreamsIEEE Transactions on Cybernetics10.1109/TCYB.2020.302397352:6(4173-4186)Online publication date: Jun-2022
https://doi.org/10.1109/TCYB.2020.3023973
Borutta FKazempour DMathy FKröger PSeidl T(2020)Detecting Arbitrarily Oriented Subspace Clusters in Data Streams Using Hough TransformAdvances in Knowledge Discovery and Data Mining10.1007/978-3-030-47426-3_28(356-368)Online publication date: 6-May-2020
https://doi.org/10.1007/978-3-030-47426-3_28
Hassani MKim YChoi SSeidl T(2015)Subspace clustering of data streamsJournal of Intelligent Information Systems10.1007/s10844-014-0319-245:3(319-335)Online publication date: 1-Dec-2015
https://dl.acm.org/doi/10.1007/s10844-014-0319-2
Hassani MKim YChoi SSeidl T(2013)Effective Evaluation Measures for Subspace Clustering of Data StreamsRevised Selected Papers of PAKDD 2013 International Workshops on Trends and Applications in Knowledge Discovery and Data Mining - Volume 786710.1007/978-3-642-40319-4_30(342-353)Online publication date: 14-Apr-2013
https://dl.acm.org/doi/10.1007/978-3-642-40319-4_30
Miller ZHu W(2012)Data Stream Subspace Clustering for Anomalous Network Packet DetectionJournal of Information Security10.4236/jis.2012.3302703:03(215-223)Online publication date: 2012
https://doi.org/10.4236/jis.2012.33027
Hassani MSpaus PGaber MSeidl T(2012)Density-Based projected clustering of data streamsProceedings of the 6th international conference on Scalable Uncertainty Management10.1007/978-3-642-33362-0_24(311-324)Online publication date: 17-Sep-2012
https://dl.acm.org/doi/10.1007/978-3-642-33362-0_24
Kriegel HKröger PNtoutsi IZimek A(2011)Density based subspace clustering over dynamic dataProceedings of the 23rd international conference on Scientific and statistical database management10.5555/2032397.2032428(387-404)Online publication date: 20-Jul-2011
https://dl.acm.org/doi/10.5555/2032397.2032428
Dunham MHahsler MSpiliopoulou M(2011)Novel data stream pattern mining report on the StreamKDD'10 workshopACM SIGKDD Explorations Newsletter10.1145/1964897.196491212:2(54-55)Online publication date: 31-Mar-2011
https://dl.acm.org/doi/10.1145/1964897.1964912
Kriegel HKröger PNtoutsi IZimek A(2011)Density Based Subspace Clustering over Dynamic DataScientific and Statistical Database Management10.1007/978-3-642-22351-8_24(387-404)Online publication date: 2011
https://doi.org/10.1007/978-3-642-22351-8_24

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