Modified cat swarm optimization for clustering

Saad Razzaq; Fahad Maqbool; Amir Hussain

doi:10.1007/978-3-319-49685-6_15

Modified cat swarm optimization for clustering

Saad Razzaq^*
, Fahad Maqbool
, Amir Hussain

^*Corresponding author for this work

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

10 Scopus citations

Abstract

Clustering is one of the most challenging optimization problems. Many Swarm Intelligence techniques including Ant Colony optimization (ACO), Particle Swarm Optimization (PSO), and Honey Bee Optimization (HBO) have been used to solve clustering. Cat Swarm Optimization (CSO) is one of the newly proposed heuristics in swarm intelligence, which is generated by observing the behavior of cats, and has been used for clustering and numerical function optimization. CSO based clustering is dependent on a pre-specified value of K i.e. Number of Clusters. In this paper we have proposed a “Modified Cat Swam Optimization (MCSO)” heuristic to discover clusters based on the nature of data rather than user specified K. MCSO performs a data scan to determine the initial cluster centers. We have compared the results of MCSO with CSO to demonstrate the enhanced efficiency and accuracy of our proposed technique.

Original language	English
Title of host publication	Advances in Brain Inspired Cognitive Systems - 8th International Conference, BICS 2016, Proceedings
Editors	Cheng-Lin Liu, Yi Zeng, Zhaoxiang Zhang, Kay Chen Tan, Bin Luo, Amir Hussain
Publisher	Springer Verlag
Pages	161-170
Number of pages	10
ISBN (Print)	9783319496849
DOIs	https://doi.org/10.1007/978-3-319-49685-6_15
State	Published - 2016
Externally published	Yes
Event	8th International Conference on Brain Inspired Cognitive Systems, BICS 2016 - Beijing, China Duration: 28 Nov 2016 → 30 Nov 2016

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	10023 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	8th International Conference on Brain Inspired Cognitive Systems, BICS 2016
Country/Territory	China
City	Beijing
Period	28/11/16 → 30/11/16

Bibliographical note

Publisher Copyright:
© Springer International Publishing AG 2016.

Keywords

Cat swarm optimization
Clustering
Swarm intelligence

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-319-49685-6_15

Cite this

Razzaq, S., Maqbool, F., & Hussain, A. (2016). Modified cat swarm optimization for clustering. In C.-L. Liu, Y. Zeng, Z. Zhang, K. C. Tan, B. Luo, & A. Hussain (Eds.), Advances in Brain Inspired Cognitive Systems - 8th International Conference, BICS 2016, Proceedings (pp. 161-170). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10023 LNAI). Springer Verlag. https://doi.org/10.1007/978-3-319-49685-6_15

Razzaq, Saad ; Maqbool, Fahad ; Hussain, Amir. / Modified cat swarm optimization for clustering. Advances in Brain Inspired Cognitive Systems - 8th International Conference, BICS 2016, Proceedings. editor / Cheng-Lin Liu ; Yi Zeng ; Zhaoxiang Zhang ; Kay Chen Tan ; Bin Luo ; Amir Hussain. Springer Verlag, 2016. pp. 161-170 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{4618d6f25ea04bc791bb816d90a46669,

title = "Modified cat swarm optimization for clustering",

abstract = "Clustering is one of the most challenging optimization problems. Many Swarm Intelligence techniques including Ant Colony optimization (ACO), Particle Swarm Optimization (PSO), and Honey Bee Optimization (HBO) have been used to solve clustering. Cat Swarm Optimization (CSO) is one of the newly proposed heuristics in swarm intelligence, which is generated by observing the behavior of cats, and has been used for clustering and numerical function optimization. CSO based clustering is dependent on a pre-specified value of K i.e. Number of Clusters. In this paper we have proposed a “Modified Cat Swam Optimization (MCSO)” heuristic to discover clusters based on the nature of data rather than user specified K. MCSO performs a data scan to determine the initial cluster centers. We have compared the results of MCSO with CSO to demonstrate the enhanced efficiency and accuracy of our proposed technique.",

keywords = "Cat swarm optimization, Clustering, Swarm intelligence",

author = "Saad Razzaq and Fahad Maqbool and Amir Hussain",

note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG 2016.; 8th International Conference on Brain Inspired Cognitive Systems, BICS 2016 ; Conference date: 28-11-2016 Through 30-11-2016",

year = "2016",

doi = "10.1007/978-3-319-49685-6\_15",

language = "English",

isbn = "9783319496849",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Verlag",

pages = "161--170",

editor = "Cheng-Lin Liu and Yi Zeng and Zhaoxiang Zhang and Tan, \{Kay Chen\} and Bin Luo and Amir Hussain",

booktitle = "Advances in Brain Inspired Cognitive Systems - 8th International Conference, BICS 2016, Proceedings",

address = "Germany",

}

Razzaq, S, Maqbool, F & Hussain, A 2016, Modified cat swarm optimization for clustering. in C-L Liu, Y Zeng, Z Zhang, KC Tan, B Luo & A Hussain (eds), Advances in Brain Inspired Cognitive Systems - 8th International Conference, BICS 2016, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10023 LNAI, Springer Verlag, pp. 161-170, 8th International Conference on Brain Inspired Cognitive Systems, BICS 2016, Beijing, China, 28/11/16. https://doi.org/10.1007/978-3-319-49685-6_15

Modified cat swarm optimization for clustering. / Razzaq, Saad; Maqbool, Fahad; Hussain, Amir.
Advances in Brain Inspired Cognitive Systems - 8th International Conference, BICS 2016, Proceedings. ed. / Cheng-Lin Liu; Yi Zeng; Zhaoxiang Zhang; Kay Chen Tan; Bin Luo; Amir Hussain. Springer Verlag, 2016. p. 161-170 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10023 LNAI).

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

TY - GEN

T1 - Modified cat swarm optimization for clustering

AU - Razzaq, Saad

AU - Maqbool, Fahad

AU - Hussain, Amir

N1 - Publisher Copyright: © Springer International Publishing AG 2016.

PY - 2016

Y1 - 2016

N2 - Clustering is one of the most challenging optimization problems. Many Swarm Intelligence techniques including Ant Colony optimization (ACO), Particle Swarm Optimization (PSO), and Honey Bee Optimization (HBO) have been used to solve clustering. Cat Swarm Optimization (CSO) is one of the newly proposed heuristics in swarm intelligence, which is generated by observing the behavior of cats, and has been used for clustering and numerical function optimization. CSO based clustering is dependent on a pre-specified value of K i.e. Number of Clusters. In this paper we have proposed a “Modified Cat Swam Optimization (MCSO)” heuristic to discover clusters based on the nature of data rather than user specified K. MCSO performs a data scan to determine the initial cluster centers. We have compared the results of MCSO with CSO to demonstrate the enhanced efficiency and accuracy of our proposed technique.

AB - Clustering is one of the most challenging optimization problems. Many Swarm Intelligence techniques including Ant Colony optimization (ACO), Particle Swarm Optimization (PSO), and Honey Bee Optimization (HBO) have been used to solve clustering. Cat Swarm Optimization (CSO) is one of the newly proposed heuristics in swarm intelligence, which is generated by observing the behavior of cats, and has been used for clustering and numerical function optimization. CSO based clustering is dependent on a pre-specified value of K i.e. Number of Clusters. In this paper we have proposed a “Modified Cat Swam Optimization (MCSO)” heuristic to discover clusters based on the nature of data rather than user specified K. MCSO performs a data scan to determine the initial cluster centers. We have compared the results of MCSO with CSO to demonstrate the enhanced efficiency and accuracy of our proposed technique.

KW - Cat swarm optimization

KW - Clustering

KW - Swarm intelligence

UR - https://www.scopus.com/pages/publications/84997419174

U2 - 10.1007/978-3-319-49685-6_15

DO - 10.1007/978-3-319-49685-6_15

M3 - Conference contribution

AN - SCOPUS:84997419174

SN - 9783319496849

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 161

EP - 170

BT - Advances in Brain Inspired Cognitive Systems - 8th International Conference, BICS 2016, Proceedings

A2 - Liu, Cheng-Lin

A2 - Zeng, Yi

A2 - Zhang, Zhaoxiang

A2 - Tan, Kay Chen

A2 - Luo, Bin

A2 - Hussain, Amir

PB - Springer Verlag

T2 - 8th International Conference on Brain Inspired Cognitive Systems, BICS 2016

Y2 - 28 November 2016 through 30 November 2016

ER -

Razzaq S, Maqbool F, Hussain A. Modified cat swarm optimization for clustering. In Liu CL, Zeng Y, Zhang Z, Tan KC, Luo B, Hussain A, editors, Advances in Brain Inspired Cognitive Systems - 8th International Conference, BICS 2016, Proceedings. Springer Verlag. 2016. p. 161-170. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-49685-6_15

Modified cat swarm optimization for clustering

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

Fingerprint

Cite this