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Fast algorithms for projected clustering

Authors:

Charu C. Aggarwal,

Cecilia Procopiuc,

Jong Soo ParkAuthors Info & Claims

ACM SIGMOD Record, Volume 28, Issue 2

Pages 61 - 72

https://doi.org/10.1145/304181.304188

Published: 01 June 1999 Publication History

Abstract

The clustering problem is well known in the database literature for its numerous applications in problems such as customer segmentation, classification and trend analysis. Unfortunately, all known algorithms tend to break down in high dimensional spaces because of the inherent sparsity of the points. In such high dimensional spaces not all dimensions may be relevant to a given cluster. One way of handling this is to pick the closely correlated dimensions and find clusters in the corresponding subspace. Traditional feature selection algorithms attempt to achieve this. The weakness of this approach is that in typical high dimensional data mining applications different sets of points may cluster better for different subsets of dimensions. The number of dimensions in each such cluster-specific subspace may also vary. Hence, it may be impossible to find a single small subset of dimensions for all the clusters. We therefore discuss a generalization of the clustering problem, referred to as the projected clustering problem, in which the subsets of dimensions selected are specific to the clusters themselves. We develop an algorithmic framework for solving the projected clustering problem, and test its performance on synthetic data.

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Cited By

Harikumar SJayamohan Pillai CVani Chithra VRaman RKaimal MTang KNedungadi P(2024)High-Dimensional Projected Clustering for Learner Competency Analysis in Medical Training ProgramsIEEE Access10.1109/ACCESS.2024.349631812(171807-171823)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3496318
Jain NGhosh SGhosh A(2024)A parameter free relative density based biclustering method for identifying non-linear feature relationsHeliyon10.1016/j.heliyon.2024.e3473610:15(e34736)Online publication date: Aug-2024
https://doi.org/10.1016/j.heliyon.2024.e34736
Zhu JLiu X(2024)An integrated intrusion detection framework based on subspace clustering and ensemble learningComputers and Electrical Engineering10.1016/j.compeleceng.2024.109113115:COnline publication date: 2-Jul-2024
https://dl.acm.org/doi/10.1016/j.compeleceng.2024.109113
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Index Terms

Fast algorithms for projected clustering
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Clustering and classification
  2. Information systems applications
    1. Data mining
      1. Clustering

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Information & Contributors

Information

Published In

cover image ACM SIGMOD Record

ACM SIGMOD Record Volume 28, Issue 2

June 1999

599 pages

ISSN:0163-5808

DOI:10.1145/304181

Chairmen:
Susan Davidson
Univ. of Pennsylvania
,
Christos Faloutsos
Carnegie Mellon Univ.

Issue’s Table of Contents

SIGMOD '99: Proceedings of the 1999 ACM SIGMOD international conference on Management of data
June 1999
604 pages
ISBN:1581130848
DOI:10.1145/304182
Chairmen:
Susan B. Davidson
Univ. of Pennsylvania, Philidelphia
,
Christos Faloutsos
Carnegie Mellon Univ., Pittsburgh

Copyright © 1999 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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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 June 1999

Published in SIGMOD Volume 28, Issue 2

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Cited By

Harikumar SJayamohan Pillai CVani Chithra VRaman RKaimal MTang KNedungadi P(2024)High-Dimensional Projected Clustering for Learner Competency Analysis in Medical Training ProgramsIEEE Access10.1109/ACCESS.2024.349631812(171807-171823)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3496318
Jain NGhosh SGhosh A(2024)A parameter free relative density based biclustering method for identifying non-linear feature relationsHeliyon10.1016/j.heliyon.2024.e3473610:15(e34736)Online publication date: Aug-2024
https://doi.org/10.1016/j.heliyon.2024.e34736
Zhu JLiu X(2024)An integrated intrusion detection framework based on subspace clustering and ensemble learningComputers and Electrical Engineering10.1016/j.compeleceng.2024.109113115:COnline publication date: 2-Jul-2024
https://dl.acm.org/doi/10.1016/j.compeleceng.2024.109113
Singh JSingh D(2024)A comprehensive review of clustering techniques in artificial intelligence for knowledge discovery: Taxonomy, challenges, applications and future prospectsAdvanced Engineering Informatics10.1016/j.aei.2024.10279962(102799)Online publication date: Oct-2024
https://doi.org/10.1016/j.aei.2024.102799
Kalita JBhattacharyya DRoy S(2024)Cluster analysisFundamentals of Data Science10.1016/B978-0-32-391778-0.00016-8(181-214)Online publication date: 2024
https://doi.org/10.1016/B978-0-32-391778-0.00016-8
Haghzad Klidbary SJavadian M(2024)A Novel Hierarchical High-Dimensional Unsupervised Active Learning MethodInternational Journal of Computational Intelligence Systems10.1007/s44196-024-00601-w17:1Online publication date: 24-Jul-2024
https://doi.org/10.1007/s44196-024-00601-w
Mondal RIgnatova EWalke DBroneske DSaake GHeyer R(2024)Clustering graph data: the roadmap to spectral techniquesDiscover Artificial Intelligence10.1007/s44163-024-00102-x4:1Online publication date: 22-Jan-2024
https://doi.org/10.1007/s44163-024-00102-x
Kishore Veparala VKalpana V(2023)Big Data y diferentes enfoques de clustering subespacial: De la promoción en redes sociales al mapeo genómicoSalud, Ciencia y Tecnología10.56294/saludcyt20234133(413)Online publication date: 19-Jun-2023
https://doi.org/10.56294/saludcyt2023413
Zhao QZhang XLiu XZong L(2023)Semi-supervised Projected Subspace Clustering2023 5th International Conference on Frontiers Technology of Information and Computer (ICFTIC)10.1109/ICFTIC59930.2023.10456306(1021-1024)Online publication date: 17-Nov-2023
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Das ADas SMukherjee J(2023)Approximation algorithms for orthogonal line centersDiscrete Applied Mathematics10.1016/j.dam.2023.05.014338:C(69-76)Online publication date: 30-Oct-2023
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