article

ROCK: a robust clustering algorithm for categorical attributes

Authors:

Sudipto Guha,

Rajeev Rastogi,

Kyuseok ShimAuthors Info & Claims

Information Systems, Volume 25, Issue 5

Pages 345 - 366

https://doi.org/10.1016/S0306-4379(00)00022-3

Published: 01 July 2000 Publication History

Abstract

No abstract available.

Cited By

View all

Xie JJiang LXia SXiang XWang G(2024)An adaptive density clustering approach with multi-granularity fusionInformation Fusion10.1016/j.inffus.2024.102273106:COnline publication date: 1-Jun-2024
https://dl.acm.org/doi/10.1016/j.inffus.2024.102273
Xie WLiu ZChen BSrivastava J(2024)Boosting cluster tree with reciprocal nearest neighbors scoringEngineering Applications of Artificial Intelligence10.1016/j.engappai.2023.107438127:PBOnline publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1016/j.engappai.2023.107438
Naik DDharavath RQi L(2024)Quantum-PSO based unsupervised clustering of users in social networks using attributesCluster Computing10.1007/s10586-023-03993-027:1(997-1015)Online publication date: 1-Feb-2024
https://dl.acm.org/doi/10.1007/s10586-023-03993-0
Show More Cited By

Index Terms

ROCK: a robust clustering algorithm for categorical attributes
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Unsupervised learning
        Cluster analysis
2. Information systems
  1. Information systems applications
    1. Data mining

Recommendations

Proficient Normalised Fuzzy K-Means With Initial Centroids Methodology

This article describes how data is relevant and if it can be organized, linked with other data and grouped into a cluster. Clustering is the process of organizing a given set of objects into a set of disjoint groups called clusters. There are a number ...
Extensions to the k-Means Algorithm for Clustering Large Data Sets with Categorical Values

The k-means algorithm is well known for its efficiency in clustering large data sets. However, working only on numeric values prohibits it from being used to cluster real world data containing categorical values. In this paper we present two algorithms ...
A clustering algorithm based on an estimated distribution model

This paper applies an estimated distribution model to clustering problems. The proposed clustering method makes use of an inter-intra cluster metric and performs a conditional split-merge operation. With conditional splitting and merging, the proposed ...

Comments

Information & Contributors

Information

Published In

Information Systems Volume 25, Issue 5

July 2000

68 pages

ISSN:0306-4379

Editors:
Matthias Jarke
RWTH Aachen, Informatik V, Aachen, Germany
,
Dennis Shasha
New York Univ., New York, NY

Issue’s Table of Contents

Publisher

Elsevier Science Ltd.

United Kingdom

Publication History

Published: 01 July 2000

Author Tags

Qualifiers

Article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

217
Total Citations
View Citations
0
Total Downloads

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

Reflects downloads up to 04 Oct 2024

Other Metrics

View Author Metrics

Citations

Cited By

View all

Xie JJiang LXia SXiang XWang G(2024)An adaptive density clustering approach with multi-granularity fusionInformation Fusion10.1016/j.inffus.2024.102273106:COnline publication date: 1-Jun-2024
https://dl.acm.org/doi/10.1016/j.inffus.2024.102273
Xie WLiu ZChen BSrivastava J(2024)Boosting cluster tree with reciprocal nearest neighbors scoringEngineering Applications of Artificial Intelligence10.1016/j.engappai.2023.107438127:PBOnline publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1016/j.engappai.2023.107438
Naik DDharavath RQi L(2024)Quantum-PSO based unsupervised clustering of users in social networks using attributesCluster Computing10.1007/s10586-023-03993-027:1(997-1015)Online publication date: 1-Feb-2024
https://dl.acm.org/doi/10.1007/s10586-023-03993-0
Jia YLu KLi XHao C(2024)SRG: a clustering algorithm based on scale division and region growingCluster Computing10.1007/s10586-022-03917-427:1(137-157)Online publication date: 1-Feb-2024
https://dl.acm.org/doi/10.1007/s10586-022-03917-4
Demirhan HBaser F(2024)Hierarchical fuzzy regression functions for mixed predictors and an application to real estate price predictionNeural Computing and Applications10.1007/s00521-024-09673-336:19(11545-11561)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1007/s00521-024-09673-3
Bhuvanya RKavitha M(2023)Image Clustering and Feature Extraction by Utilizing an Improvised Unsupervised Learning ApproachCybernetics and Information Technologies10.2478/cait-2023-001023:2(3-19)Online publication date: 1-Jun-2023
https://dl.acm.org/doi/10.2478/cait-2023-0010
Wang QWang FRen FLi ZNie F(2023)An Effective Clustering Optimization Method for Unsupervised Linear Discriminant AnalysisIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2021.312402335:4(3444-3457)Online publication date: 1-Apr-2023
https://dl.acm.org/doi/10.1109/TKDE.2021.3124023
Zhang CChen LZhao YWang YChen C(2023)Graph Enhanced Fuzzy Clustering for Categorical Data Using a Bayesian Dissimilarity MeasureIEEE Transactions on Fuzzy Systems10.1109/TFUZZ.2022.318983131:3(810-824)Online publication date: 1-Mar-2023
https://dl.acm.org/doi/10.1109/TFUZZ.2022.3189831
Sugahara KOkamoto K(2023)Hierarchical co-clustering with augmented matrices from external domainsPattern Recognition10.1016/j.patcog.2023.109657142:COnline publication date: 1-Oct-2023
https://dl.acm.org/doi/10.1016/j.patcog.2023.109657
Wang CChi CYao LLiew AShen H(2023)Interdependence analysis on heterogeneous data via behavior interior dimensionsKnowledge-Based Systems10.1016/j.knosys.2023.110893279:COnline publication date: 4-Nov-2023
https://dl.acm.org/doi/10.1016/j.knosys.2023.110893
Show More Cited By

View Options

View options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Abstract

Cited By

Index Terms

Recommendations

Proficient Normalised Fuzzy K-Means With Initial Centroids Methodology

Extensions to the k-Means Algorithm for Clustering Large Data Sets with Categorical Values

A clustering algorithm based on an estimated distribution model

Comments

Information

Published In

Publisher

Publication History

Author Tags

Qualifiers

Contributors

Other Metrics

Bibliometrics

Article Metrics

Other Metrics

Citations

Cited By

View options

Get Access

Login options

Full Access

Figures

Other

Share

Share this Publication link

Share on social media

Affiliations