NDLI: A new hybrid approach for data clustering

Content Provider	IEEE Xplore Digital Library
Author	Yazdani, D. Golyari, S. Meybodi, M.R.
Copyright Year	2010
Description	Author affiliation: Department of Computer Engineering and Information Technology, Amirkabir University of Technology, Tehran, Iran (Meybodi, M.R.) \|\| Islamic azad university, Shirvan Branch, Iran (Yazdani, D.; Golyari, S.)
Abstract	Data clustering has been applied in multiple fields such as machine learning, data mining, wireless sensor networks and pattern recognition. One of the most famous clustering approaches is K-means which effectively has been used in many clustering problems, but this algorithm has some problems such as local optimal convergence and initial point sensitivity. Artificial fishes swarm algorithm (AFSA) is one of the swarm intelligent algorithms and its major application is in solving optimization problems. Of its characteristics, it can refer to high convergent rate and insensitivity to initial values. In this paper a hybrid clustering method based on artificial fishes swarm algorithm and K-means so called KAFSA is proposed. In the proposed algorithm, K-means algorithm is used as one of the behaviors of artificial fishes in AFSA. The proposed algorithm has been tested on five data sets and its efficiency was compared with particle swarm optimization (PSO), K-means and standard AFSA algorithms. Experimental results showed that proposed approach has suitable and acceptable efficacy in data clustering.
Starting Page	914
Ending Page	919
File Size	1320900
Page Count	6
File Format	PDF
ISBN	9781424481835
DOI	10.1109/ISTEL.2010.5734153
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-12-04
Publisher Place	Iran
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Algorithm design and analysis K-means Visualization Art Error analysis optimization Clustering algorithms data clustering Artificial fishes swarm algorithm Marine animals PSO Convergence
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Combining PSO and k-means to enhance data clustering

A Clustering K-means Algorithm Based on Improved PSO Algorithm

A new hybrid learning-based algorithm for data clustering

Anomaly Intrusion Detection Method Based on K-Means Clustering Algorithm with Particle Swarm Optimization

FuzzifiedPSO and K-Harmonic means algorithm for electrical data clustering

A new hybrid algorithm for optimization based on artificial fish swarm algorithm and cellular learning automata

Key frame extraction based on Artificial Fish Swarm Algorithm and k-means

A new hybrid approach for data clustering using firefly algorithm and K-means

Cooperative swarm based clustering algorithm based on PSO and k-means to find optimal cluster centroids

A new hybrid approach for data clustering

Similar Documents

Combining PSO and k-means to enhance data clustering

A Clustering K-means Algorithm Based on Improved PSO Algorithm

A new hybrid learning-based algorithm for data clustering

Anomaly Intrusion Detection Method Based on K-Means Clustering Algorithm with Particle Swarm Optimization

FuzzifiedPSO and K-Harmonic means algorithm for electrical data clustering

A new hybrid algorithm for optimization based on artificial fish swarm algorithm and cellular learning automata

Key frame extraction based on Artificial Fish Swarm Algorithm and k-means

A new hybrid approach for data clustering using firefly algorithm and K-means

Cooperative swarm based clustering algorithm based on PSO and k-means to find optimal cluster centroids

A new hybrid approach for data clustering