research-article

Joint cluster analysis of attribute data and relationship data: The connected k-center problem, algorithms and applications

Authors:

Binay Bhattacharya,

Boaz Ben-MosheAuthors Info & Claims

ACM Transactions on Knowledge Discovery from Data (TKDD), Volume 2, Issue 2

Article No.: 7, Pages 1 - 35

https://doi.org/10.1145/1376815.1376816

Published: 24 July 2008 Publication History

Abstract

Attribute data and relationship data are two principal types of data, representing the intrinsic and extrinsic properties of entities. While attribute data have been the main source of data for cluster analysis, relationship data such as social networks or metabolic networks are becoming increasingly available. It is also common to observe both data types carry complementary information such as in market segmentation and community identification, which calls for a joint cluster analysis of both data types so as to achieve better results. In this article, we introduce the novel Connected k-Center (CkC) problem, a clustering model taking into account attribute data as well as relationship data. We analyze the complexity of the problem and prove its NP-hardness. Therefore, we analyze the approximability of the problem and also present a constant factor approximation algorithm. For the special case of the CkC problem where the relationship data form a tree structure, we propose a dynamic programming method giving an optimal solution in polynomial time. We further present NetScan, a heuristic algorithm that is efficient and effective for large real databases. Our extensive experimental evaluation on real datasets demonstrates the meaningfulness and accuracy of the NetScan results.

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Alfaqeeh MSkillicorn DAlfaqeeh MSkillicorn D(2024)BackgroundFinding Communities in Social Networks Using Graph Embeddings10.1007/978-3-031-60916-9_2(17-36)Online publication date: 29-Apr-2024
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Index Terms

Joint cluster analysis of attribute data and relationship data: The connected k-center problem, algorithms and applications
1. Information systems
  1. Information systems applications
    1. Data mining

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cover image ACM Transactions on Knowledge Discovery from Data

ACM Transactions on Knowledge Discovery from Data Volume 2, Issue 2

July 2008

152 pages

ISSN:1556-4681

EISSN:1556-472X

DOI:10.1145/1376815

Issue’s Table of Contents

Copyright © 2008 ACM.

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Publication History

Published: 24 July 2008

Accepted: 01 December 2007

Revised: 01 July 2007

Received: 01 December 2006

Published in TKDD Volume 2, Issue 2

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Cited By

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https://doi.org/10.1007/s00453-024-01266-9
Alfaqeeh MSkillicorn DAlfaqeeh MSkillicorn D(2024)BackgroundFinding Communities in Social Networks Using Graph Embeddings10.1007/978-3-031-60916-9_2(17-36)Online publication date: 29-Apr-2024
https://doi.org/10.1007/978-3-031-60916-9_2
Li RYang Z(2021)The Nearest Neighbor Algorithm for Balanced and Connected k-Center Problem under Modular Distance2021 International Conference on Networking and Network Applications (NaNA)10.1109/NaNA53684.2021.00073(385-389)Online publication date: Oct-2021
https://doi.org/10.1109/NaNA53684.2021.00073
Chunaev P(2020)Community detection in node-attributed social networks: A surveyComputer Science Review10.1016/j.cosrev.2020.10028637(100286)Online publication date: Aug-2020
https://doi.org/10.1016/j.cosrev.2020.100286
Combe DLargeron CJeudy BFogelman‐Soulié FWang J(2020)Attributed Networks Partitioning Based on Modularity OptimizationAdvances in Data Science10.1002/9781119695110.ch8(169-185)Online publication date: 15-Jan-2020
https://doi.org/10.1002/9781119695110.ch8
Luo SZhang ZZhang YMa S(2019)Co-Association Matrix-Based Multi-Layer Fusion for Community Detection in Attributed NetworksEntropy10.3390/e2101009521:1(95)Online publication date: 20-Jan-2019
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Atzmueller MSoldano HSantini GBouthinon D(2019)MinerLSD: efficient mining of local patterns on attributed networksApplied Network Science10.1007/s41109-019-0155-y4:1Online publication date: 27-Jun-2019
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Azaouzi MRhouma DBen Romdhane L(2019)Community detection in large-scale social networks: state-of-the-art and future directionsSocial Network Analysis and Mining10.1007/s13278-019-0566-x9:1Online publication date: 18-May-2019
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