Auto-Clustering using Particle Swarm Optimization and Bacterial Foraging

Jakob Rutkowski Olesen; Jorge Cordero; Yifeng Zeng

doi:10.1007/978-3-642-03603-3_6

Auto-Clustering using Particle Swarm Optimization and Bacterial Foraging

Jakob Rutkowski Olesen, Jorge Cordero, Yifeng Zeng

Institut for Datalogi

Publikation: Bidrag til tidsskrift › Konferenceartikel i tidsskrift › Forskning › peer review

12 Citationer (Scopus)

Abstract

This paper presents a hybrid approach for clustering based on particle swarm optimization (PSO) and bacteria foraging algorithms (BFA). The new method AutoCPB (Auto-Clustering based on particle bacterial foraging) makes use of autonomous agents whose primary objective is to cluster chunks of data by using simplistic collaboration. Inspired by the advances in clustering using particle swarm optimization, we suggest further improvements. Moreover, we gathered standard benchmark datasets and compared our new approach against the standard K-means algorithm, obtaining promising results. Our hybrid mechanism outperforms earlier PSO-based approaches by using simplistic communication between agents.

Originalsprog	Engelsk
Bogserie	Lecture Notes in Computer Science
Vol/bind	5680
Sider (fra-til)	69-83
ISSN	0302-9743
DOI	https://doi.org/10.1007/978-3-642-03603-3_6
Status	Udgivet - 2009
Begivenhed	Agents and Data Mining Interaction, ADMI 2009 - Budapest, Ungarn Varighed: 10 maj 2009 → 15 maj 2009 Konferencens nummer: 4

Konference

Konference	Agents and Data Mining Interaction, ADMI 2009
Nummer	4
Land/Område	Ungarn
By	Budapest
Periode	10/05/2009 → 15/05/2009

Bibliografisk note

Titel:
Agents and Data Mining Interaction: 4th International Workshop, ADMI 2009, Budapest, Hungary, May 10-15,2009, Revised Selected Papers

Oversat titel:

Oversat undertitel:

Forlag:
Springer

ISBN (Trykt):
978-3-642-03602-6

ISBN (Elektronisk):

Publikationsserier:
Lecture Notes In Artificial Intelligence, Springer Verlag, 0302-9743, 1611-3349, 5680

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Citationsformater

@inproceedings{8c00d5f01afc11dfa996000ea68e967b,

title = "Auto-Clustering using Particle Swarm Optimization and Bacterial Foraging",

abstract = "This paper presents a hybrid approach for clustering based on particle swarm optimization (PSO) and bacteria foraging algorithms (BFA). The new method AutoCPB (Auto-Clustering based on particle bacterial foraging) makes use of autonomous agents whose primary objective is to cluster chunks of data by using simplistic collaboration. Inspired by the advances in clustering using particle swarm optimization, we suggest further improvements. Moreover, we gathered standard benchmark datasets and compared our new approach against the standard K-means algorithm, obtaining promising results. Our hybrid mechanism outperforms earlier PSO-based approaches by using simplistic communication between agents.",

author = "{Rutkowski Olesen}, Jakob and Jorge Cordero and Yifeng Zeng",

note = "Titel: Agents and Data Mining Interaction: 4th International Workshop, ADMI 2009, Budapest, Hungary, May 10-15,2009, Revised Selected Papers Oversat titel: Oversat undertitel: Forlag: Springer ISBN (Trykt): 978-3-642-03602-6 ISBN (Elektronisk): Publikationsserier: Lecture Notes In Artificial Intelligence, Springer Verlag, 0302-9743, 1611-3349, 5680 ; Agents and Data Mining Interaction, ADMI 2009 ; Conference date: 10-05-2009 Through 15-05-2009",

year = "2009",

doi = "10.1007/978-3-642-03603-3_6",

language = "English",

volume = "5680",

pages = "69--83",

journal = "Lecture Notes in Computer Science",

issn = "0302-9743",

publisher = "Physica-Verlag",

}

TY - GEN

T1 - Auto-Clustering using Particle Swarm Optimization and Bacterial Foraging

AU - Rutkowski Olesen, Jakob

AU - Cordero, Jorge

AU - Zeng, Yifeng

N1 - Conference code: 4

PY - 2009

Y1 - 2009

N2 - This paper presents a hybrid approach for clustering based on particle swarm optimization (PSO) and bacteria foraging algorithms (BFA). The new method AutoCPB (Auto-Clustering based on particle bacterial foraging) makes use of autonomous agents whose primary objective is to cluster chunks of data by using simplistic collaboration. Inspired by the advances in clustering using particle swarm optimization, we suggest further improvements. Moreover, we gathered standard benchmark datasets and compared our new approach against the standard K-means algorithm, obtaining promising results. Our hybrid mechanism outperforms earlier PSO-based approaches by using simplistic communication between agents.

AB - This paper presents a hybrid approach for clustering based on particle swarm optimization (PSO) and bacteria foraging algorithms (BFA). The new method AutoCPB (Auto-Clustering based on particle bacterial foraging) makes use of autonomous agents whose primary objective is to cluster chunks of data by using simplistic collaboration. Inspired by the advances in clustering using particle swarm optimization, we suggest further improvements. Moreover, we gathered standard benchmark datasets and compared our new approach against the standard K-means algorithm, obtaining promising results. Our hybrid mechanism outperforms earlier PSO-based approaches by using simplistic communication between agents.

U2 - 10.1007/978-3-642-03603-3_6

DO - 10.1007/978-3-642-03603-3_6

M3 - Conference article in Journal

SN - 0302-9743

VL - 5680

SP - 69

EP - 83

JO - Lecture Notes in Computer Science

JF - Lecture Notes in Computer Science

T2 - Agents and Data Mining Interaction, ADMI 2009

Y2 - 10 May 2009 through 15 May 2009

ER -

Auto-Clustering using Particle Swarm Optimization and Bacterial Foraging

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