Distributed and incremental clustering based on weighted affinity propagation

Xiangliang Zhang; Cyril Furtlehner; Michèle Sebag

doi:10.3233/978-1-58603-893-9-199

Distributed and incremental clustering based on weighted affinity propagation

Xiangliang Zhang^*, Cyril Furtlehner, Michèle Sebag

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

5 Scopus citations

Abstract

A new clustering algorithm Affinity Propagation (AP) is hindered by its quadratic complexity. The Weighted Affinity Propagation (WAP) proposed in this paper is used to eliminate this limitation, support two scalable algorithms. Distributed AP clustering handles large datasets by merging the exemplars learned from subsets. Incremental AP extends AP to online clustering of data streams. The paper validates all proposed algorithms on benchmark and on real-world datasets. Experimental results show that the proposed approaches offer a good trade-off between computational effort and performance.

Original language	English (US)
Title of host publication	STAIRS 2008. Proceedings of the Fourth Starting AI Researchers' Symposium
Publisher	IOS Press BV
Pages	199-210
Number of pages	12
Edition	1
ISBN (Print)	9781586038939
DOIs	https://doi.org/10.3233/978-1-58603-893-9-199
State	Published - 2008
Externally published	Yes

Publication series

Name	Frontiers in Artificial Intelligence and Applications
Number	1
Volume	179
ISSN (Print)	0922-6389
ISSN (Electronic)	1879-8314

Bibliographical note

Generated from Scopus record by KAUST IRTS on 2023-09-21

Keywords

Affinity Propagation
Data Clustering
Data Streaming
K-centers

ASJC Scopus subject areas

Artificial Intelligence

Access to Document

10.3233/978-1-58603-893-9-199

Cite this

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title = "Distributed and incremental clustering based on weighted affinity propagation",

abstract = "A new clustering algorithm Affinity Propagation (AP) is hindered by its quadratic complexity. The Weighted Affinity Propagation (WAP) proposed in this paper is used to eliminate this limitation, support two scalable algorithms. Distributed AP clustering handles large datasets by merging the exemplars learned from subsets. Incremental AP extends AP to online clustering of data streams. The paper validates all proposed algorithms on benchmark and on real-world datasets. Experimental results show that the proposed approaches offer a good trade-off between computational effort and performance.",

keywords = "Affinity Propagation, Data Clustering, Data Streaming, K-centers",

author = "Xiangliang Zhang and Cyril Furtlehner and Mich{\`e}le Sebag",

note = "Generated from Scopus record by KAUST IRTS on 2023-09-21",

year = "2008",

doi = "10.3233/978-1-58603-893-9-199",

language = "English (US)",

isbn = "9781586038939",

series = "Frontiers in Artificial Intelligence and Applications",

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}

Distributed and incremental clustering based on weighted affinity propagation. / Zhang, Xiangliang; Furtlehner, Cyril; Sebag, Michèle.
STAIRS 2008. Proceedings of the Fourth Starting AI Researchers' Symposium. 1. ed. IOS Press BV, 2008. p. 199-210 (Frontiers in Artificial Intelligence and Applications; Vol. 179, No. 1).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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T1 - Distributed and incremental clustering based on weighted affinity propagation

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AU - Furtlehner, Cyril

AU - Sebag, Michèle

N1 - Generated from Scopus record by KAUST IRTS on 2023-09-21

PY - 2008

Y1 - 2008

N2 - A new clustering algorithm Affinity Propagation (AP) is hindered by its quadratic complexity. The Weighted Affinity Propagation (WAP) proposed in this paper is used to eliminate this limitation, support two scalable algorithms. Distributed AP clustering handles large datasets by merging the exemplars learned from subsets. Incremental AP extends AP to online clustering of data streams. The paper validates all proposed algorithms on benchmark and on real-world datasets. Experimental results show that the proposed approaches offer a good trade-off between computational effort and performance.

AB - A new clustering algorithm Affinity Propagation (AP) is hindered by its quadratic complexity. The Weighted Affinity Propagation (WAP) proposed in this paper is used to eliminate this limitation, support two scalable algorithms. Distributed AP clustering handles large datasets by merging the exemplars learned from subsets. Incremental AP extends AP to online clustering of data streams. The paper validates all proposed algorithms on benchmark and on real-world datasets. Experimental results show that the proposed approaches offer a good trade-off between computational effort and performance.

KW - Affinity Propagation

KW - Data Clustering

KW - Data Streaming

KW - K-centers

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AN - SCOPUS:84875939206

SN - 9781586038939

T3 - Frontiers in Artificial Intelligence and Applications

SP - 199

EP - 210

BT - STAIRS 2008. Proceedings of the Fourth Starting AI Researchers' Symposium

PB - IOS Press BV

ER -

Distributed and incremental clustering based on weighted affinity propagation

Abstract

Publication series

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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