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Clustering in massive data sets

Author:

Fionn MurtaghAuthors Info & Claims

Handbook of massive data sets

January 2002

Pages 501 - 543

Published: 01 January 2002 Publication History

Abstract

We review the time and storage costs of search and clustering algorithms. We exemplify these, based on case-studies in astronomy, information retrieval, visual user interfaces, chemical databases, and other areas. Theoretical results developed as far back as the 1960s still very often remain topical. More recent work is also covered in this article. This includes a solution for the statistical question of how many clusters there are in a dataset. We also look at one line of inquiry in the use of clustering for human-computer user interfaces. Finally, the visualization of data leads to the consideration of data arrays as images, and we speculate on future results to be expected here.

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Cited By

Chalamalla AIlyas IOuzzani MPapotti PDyreson CLi FÖzsu M(2014)Descriptive and prescriptive data cleaningProceedings of the 2014 ACM SIGMOD International Conference on Management of Data10.1145/2588555.2610520(445-456)Online publication date: 18-Jun-2014
https://dl.acm.org/doi/10.1145/2588555.2610520
Olman VMao FWu HXu Y(2009)Parallel Clustering Algorithm for Large Data Sets with Applications in BioinformaticsIEEE/ACM Transactions on Computational Biology and Bioinformatics10.1109/TCBB.2007.702726:2(344-352)Online publication date: 1-Apr-2009
https://dl.acm.org/doi/10.1109/TCBB.2007.70272
Valsalam VMiikkulainen RRyan C(2008)Modular neuroevolution for multilegged locomotionProceedings of the 10th annual conference on Genetic and evolutionary computation10.1145/1389095.1389136(265-272)Online publication date: 13-Jul-2008
https://dl.acm.org/doi/10.1145/1389095.1389136
Show More Cited By

Index Terms

Clustering in massive data sets
1. Computing methodologies
2. Information systems
  1. Information retrieval
    1. Information retrieval query processing
    2. Retrieval tasks and goals
      1. Clustering and classification
  2. Information systems applications
    1. Data mining
      1. Clustering

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Published In

cover image Guide books

Handbook of massive data sets

January 2002

1252 pages

ISBN:1402004893

Editors:
James Abello
AT&T Labs Research
,
Panos M. Pardalos
University of Florida
,
Mauricio G. C. Resende
AT&T Labs Research

Publisher

Kluwer Academic Publishers

United States

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Published: 01 January 2002

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Cited By

Chalamalla AIlyas IOuzzani MPapotti PDyreson CLi FÖzsu M(2014)Descriptive and prescriptive data cleaningProceedings of the 2014 ACM SIGMOD International Conference on Management of Data10.1145/2588555.2610520(445-456)Online publication date: 18-Jun-2014
https://dl.acm.org/doi/10.1145/2588555.2610520
Olman VMao FWu HXu Y(2009)Parallel Clustering Algorithm for Large Data Sets with Applications in BioinformaticsIEEE/ACM Transactions on Computational Biology and Bioinformatics10.1109/TCBB.2007.702726:2(344-352)Online publication date: 1-Apr-2009
https://dl.acm.org/doi/10.1109/TCBB.2007.70272
Valsalam VMiikkulainen RRyan C(2008)Modular neuroevolution for multilegged locomotionProceedings of the 10th annual conference on Genetic and evolutionary computation10.1145/1389095.1389136(265-272)Online publication date: 13-Jul-2008
https://dl.acm.org/doi/10.1145/1389095.1389136
Mierswa IWurst MCattolico M(2006)Information preserving multi-objective feature selection for unsupervised learningProceedings of the 8th annual conference on Genetic and evolutionary computation10.1145/1143997.1144248(1545-1552)Online publication date: 8-Jul-2006
https://dl.acm.org/doi/10.1145/1143997.1144248
Rinfret DO'Neil PO'Neil E(2001)Bit-sliced index arithmeticACM SIGMOD Record10.1145/376284.37566930:2(47-57)Online publication date: 1-May-2001
https://dl.acm.org/doi/10.1145/376284.375669
Rinfret DO'Neil PO'Neil E(2001)Bit-sliced index arithmeticProceedings of the 2001 ACM SIGMOD international conference on Management of data10.1145/375663.375669(47-57)Online publication date: 1-May-2001
https://dl.acm.org/doi/10.1145/375663.375669

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