• [email protected]
  • +971 507 888 742
Submit Manuscript
SciAlert
  • Home
  • Journals
  • Information
    • For Authors
    • For Referees
    • For Librarian
    • For Societies
  • Contact
  1. Journal of Artificial Intelligence
  2. Vol 7 (1), 2014
  3. 13-23
  • Online First
  • Current Issue
  • Previous Issues
  • More Information
    Aims and Scope Editorial Board Guide to Authors Article Processing Charges
    Submit a Manuscript

Journal of Artificial Intelligence

Year: 2014 | Volume: 7 | Issue: 1 | Page No.: 13-23
DOI: 10.3923/jai.2014.13.23

Facebook Twitter Digg Reddit Linkedin StumbleUpon E-mail

Article Trend



Total views 268

Authors


Ali-Asghar Gholami

Country: Iran

Ramin Ayanzadeh

Country: Iran

Elaheh Raisi

Country: Iran

Keywords


  • pattern recognition
  • meta-heuristics
  • data-mining
  • honey bees foraging optimisations
  • fuzzy sets
  • Clustering
Research Article

Fuzzy Honey Bees Foraging Optimization: Swarm Intelligence Approach for Clustering

Ali-Asghar Gholami, Ramin Ayanzadeh and Elaheh Raisi
Clustering is one of the most important steps in data mining; it is known for its phenomenal functionalities in complex real world applications including biology, basic science, medicine, engineering and social science. In this sense, owing to the remarkable effects of clustering on data mining area, wide varieties of clustering approaches have been introduced to cluster data into significant subsets in order to obtain useful information. In this study, a novel clustering method based on honey bees foraging optimization algorithm and fuzzy rules is proposed. In the proposed method, fine shade of local and global search in honey bees optimization algorithm is schemed to be applied to improve the clustering efficiency. Furthermore, fuzzy operators are employed to enhance the performance of new proposed approach and prevent premature convergence. To verify and validate the functionality proposed of method, new method is run on three known data sets of the UCI Machine Learning Repository. Results of clustering reveal that proposed method estimate more desirable clusters compared to the state of the art clustering methods. Moreover, this method appears very stable in multiple tests.
PDF Fulltext XML References Citation

How to cite this article

Ali-Asghar Gholami, Ramin Ayanzadeh and Elaheh Raisi, 2014. Fuzzy Honey Bees Foraging Optimization: Swarm Intelligence Approach for Clustering. Journal of Artificial Intelligence, 7: 13-23.

DOI: 10.3923/jai.2014.13.23

URL: https://scialert.net/abstract/?doi=jai.2014.13.23

Related Articles

Fuzzy Cellular Automata Based Random Numbers Generation
Honey Bees Foraging Optimization for Mixed Nash Equilibrium Estimation
Innovative Approach to Generate Uniform Random Numbers Based on a Novel Cellular Automata
Two-layer Cellular Automata Based Cryptography
Central Limit Theorem based Cellular Automata for Generating Normal Random Numbers

Leave a Comment


Your email address will not be published. Required fields are marked *

Useful Links

  • Journals
  • For Authors
  • For Referees
  • For Librarian
  • For Socities

Contact Us

Office Number 1128,
Tamani Arts Building,
Business Bay,
Deira, Dubai, UAE

Phone: +971 507 888 742
Email: [email protected]

About Science Alert

Science Alert is a technology platform and service provider for scholarly publishers, helping them to publish and distribute their content online. We provide a range of services, including hosting, design, and digital marketing, as well as analytics and other tools to help publishers understand their audience and optimize their content. Science Alert works with a wide variety of publishers, including academic societies, universities, and commercial publishers.

Follow Us
© Copyright Science Alert. All Rights Reserved