Article

Incremental Clustering for Mining in a Data Warehousing Environment

Authors:

Hans-Peter Kriegel,

Michael Wimmer,

Xiaowei XuAuthors Info & Claims

VLDB '98: Proceedings of the 24rd International Conference on Very Large Data Bases

Pages 323 - 333

Published: 24 August 1998 Publication History

Abstract

No abstract available.

References

[1]

{AF 96} Allard D. and Fraley C.: "Non Parametric Maximum Likelihood Estimation of Features in Saptial Point Process Using Voronoi Tessellation", Journal of the American Statistical Association, December 1997. {also http://www.stat.washington.edu/tech.reports/ tr293R.ps}.

[2]

{AS 94} Rakesh Agrawal, Ramakrishnan Srikant: Fast Algorithms for Mining Association Rules in Large Databases. VLDB 1994: 487-499.

Digital Library

[3]

{BKSS 90} Norbert Beckmann, Hans-Peter Kriegel, Ralf Schneider, Bernhard Seeger: The R*-Tree: An Efficient and Robust Access Method for Points and Rectangles. SIGMOD Conference 1990: 322-331.

Digital Library

[4]

{Bou 96} Athman Bouguettaya: On-Line Clustering. IEEE Trans. Knowl. Data Eng. 8(2): 333-339(1996).

Digital Library

[5]

{CHNW 96} David Wai-Lok Cheung, Jiawei Han, Vincent T. Y. Ng, C. Y. Wong: Maintenance of Discovered Association Rules in Large Databases: An Incremental Updating Technique. ICDE 1996: 106-114.

Digital Library

[6]

{CPZ 97} Paolo Ciaccia, Marco Patella, Pavel Zezula: M-tree: An Efficient Access Method for Similarity Search in Metric Spaces. VLDB 1997: 426-435.

Digital Library

[7]

{EKSX 96} Martin Ester, Hans-Peter Kriegel, Jörg Sander, Xiaowei Xu: A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise. KDD 1996: 226-231.

[8]

{EKX 95} Martin Ester, Hans-Peter Kriegel, Xiaowei Xu: Knowledge Discovery in Large Spatial Databases: Focusing Techniques for Efficient Class Identification. SSD 1995: 67-82.

Digital Library

[9]

{EW 98} Martin Ester, Rüdiger Wittmann: Incremental Generalization for Mining in a Data Warehousing Environment. EDBT 1998: 135-149.

Digital Library

[10]

{FAAM 97} Ronen Feldman, Yonatan Aumann, Amihood Amir, Heikki Mannila: Efficient Algorithms for Discovering Frequent Sets in Incremental Databases. DMKD 1997: 0-.

[11]

{FPS 96} Usama M. Fayyad, Gregory Piatetsky-Shapiro, Padhraic Smyth: Knowledge Discovery and Data Mining: Towards a Unifying Framework. KDD 1996: 82-88.

[12]

{Gue 94} Ralf Hartmut Güting: An Introduction to Spatial Database Systems. VLDB J. 3(4): 357-399(1994).

Digital Library

[13]

{HCC 93} Jiawei Han, Yandong Cai, Nick Cercone: Data-Driven Discovery of Quantitative Rules in Relational Databases. IEEE Trans. Knowl. Data Eng. 5(1): 29-40(1993).

Digital Library

[14]

{Huy 97} Nam Huyn: Multiple-View Self-Maintenance in Data Warehousing Environments. VLDB 1997: 26-35.

Digital Library

[15]

{KR 90} L. Kaufman, P. J. Rousseeuw: Finding Groups in Data: An Introduction to Cluster Analysis. John Wiley 1990.

[16]

{Luo 95} Luotonen A.: "The common log file format", http://www.w3.org/pub/WWW/, 1995.

[17]

{MJHS 96} Mombasher B., Jain N., Han E.-H., Srivastava J." "Web Mining: Pattern Discovery from World Wide Web Transactions", Technical Report 96-050, University of Minnesota, 1996.

[18]

{MQM 97} Inderpal Singh Mumick, Dallan Quass, Barinderpal Singh Mumick: Maintenance of Data Cubes and Summary Tables in a Warehouse. SIGMOD Conference 1997: 100-111.

Digital Library

[19]

{NH 94} Raymond T. Ng, Jiawei Han: Efficient and Effective Clustering Methods for Spatial Data Mining. VLDB 1994: 144- 155.

Digital Library

[20]

{SEKX 98} Sander J., Ester M., Kriegel H.-P., Xu X.: "Density-Based Clustering in Spatial Databases: The Algorithm GDBSCAN and its Applications", will appear in: Data Mining and Knowledge Discovery, Kluwer Acedemic Publishers, Vol. 2, 1998.

Digital Library

[21]

{Sib 73} R. Sibson: SLINK: An Optimally Efficient Algorithm for the Single-Link Cluster Method. Comput. J. 16(1): 30-34 (1973).

[22]

{ZRL 96} Tian Zhang, Raghu Ramakrishnan, Miron Livny: BIRCH: An Efficient Data Clustering Method for Very Large Databases. SIGMOD Conference 1996: 103-114.

Digital Library

Cited By

Kim BKoo KEnkhbat UMoon BIves ZBonifati AEl Abbadi A(2022)DenForest: Enabling Fast Deletion in Incremental Density-Based Clustering over Sliding WindowsProceedings of the 2022 International Conference on Management of Data10.1145/3514221.3517833(296-309)Online publication date: 10-Jun-2022
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Silva PYue WRabl TGascon-Samson JZhang KDaudjee KKemme B(2020)Incremental stream query analyticsProceedings of the 14th ACM International Conference on Distributed and Event-based Systems10.1145/3401025.3401756(187-192)Online publication date: 13-Jul-2020
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Incremental Clustering for Mining in a Data Warehousing Environment
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Unsupervised learning
2. Information systems
  1. Information systems applications

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cover image DL Hosted proceedings

VLDB '98: Proceedings of the 24rd International Conference on Very Large Data Bases

August 1998

695 pages

ISBN:1558605665

Publisher

Morgan Kaufmann Publishers Inc.

San Francisco, CA, United States

Publication History

Published: 24 August 1998

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VLDB98

VLDB98: International Conference on Very Large Databases

August 24 - 27, 1998

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Cited By

Kim BKoo KEnkhbat UMoon BIves ZBonifati AEl Abbadi A(2022)DenForest: Enabling Fast Deletion in Incremental Density-Based Clustering over Sliding WindowsProceedings of the 2022 International Conference on Management of Data10.1145/3514221.3517833(296-309)Online publication date: 10-Jun-2022
https://dl.acm.org/doi/10.1145/3514221.3517833
Silva PYue WRabl TGascon-Samson JZhang KDaudjee KKemme B(2020)Incremental stream query analyticsProceedings of the 14th ACM International Conference on Distributed and Event-based Systems10.1145/3401025.3401756(187-192)Online publication date: 13-Jul-2020
https://dl.acm.org/doi/10.1145/3401025.3401756
Hasan MOrgun MSchwitter R(2018)A survey on real-time event detection from the Twitter data streamJournal of Information Science10.1177/016555151769856444:4(443-463)Online publication date: 1-Aug-2018
https://dl.acm.org/doi/10.1177/0165551517698564
Miyata YIshikawa HChbeir RIshikawa HSumiya KHatano KKoeppen M(2018)Accelerating analysis of frequently gathered data with continuous density based clusteringProceedings of the 10th International Conference on Management of Digital EcoSystems10.1145/3281375.3281380(196-203)Online publication date: 25-Sep-2018
https://dl.acm.org/doi/10.1145/3281375.3281380
Ma LVan Aken DHefny AMezerhane GPavlo AGordon GDas GJermaine CBernstein P(2018)Query-based Workload Forecasting for Self-Driving Database Management SystemsProceedings of the 2018 International Conference on Management of Data10.1145/3183713.3196908(631-645)Online publication date: 27-May-2018
https://dl.acm.org/doi/10.1145/3183713.3196908
Damiani MHachem FIssa HRanc NMoorcroft PCagnacci F(2018)Cluster-based trajectory segmentation with local noiseData Mining and Knowledge Discovery10.1007/s10618-018-0561-232:4(1017-1055)Online publication date: 1-Jul-2018
https://dl.acm.org/doi/10.1007/s10618-018-0561-2
Gan JTao YChirkova RYang JSuciu D(2017)Dynamic Density Based ClusteringProceedings of the 2017 ACM International Conference on Management of Data10.1145/3035918.3064050(1493-1507)Online publication date: 9-May-2017
https://dl.acm.org/doi/10.1145/3035918.3064050
Loo HJoseph SMarsono M(2016)Online incremental learning for high bandwidth network traffic classificationApplied Computational Intelligence and Soft Computing10.1155/2016/14658102016(1-1)Online publication date: 1-Jan-2016
https://dl.acm.org/doi/10.1155/2016/1465810
De Melo Borges JBudde MPeters ORiedel TSchankin ABeigl MYonezawa T(2016)EstaVisProceedings of the Second International Conference on IoT in Urban Space10.1145/2962735.2962749(65-70)Online publication date: 24-May-2016
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Badiozamany SOrsborn KRisch T(2016)Framework for real-time clustering over sliding windowsProceedings of the 28th International Conference on Scientific and Statistical Database Management10.1145/2949689.2949696(1-13)Online publication date: 18-Jul-2016
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