Learning cluster-based classification systems with ant colony optimization algorithms

Author:

Salama Khalid M.,Abdelbar Ashraf M.

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence

Reference52 articles.

1. Abdallah, Z. S., Gaber, M., Srinivasan, B., & Krishnaswamy, S. (2012). CBARS: Cluster based classification for activity recognition systems. In A. Hassanien, A. Salem, R. Ramadan & T. Kim (Eds.), Advanced machine learning technologies and applications (Proceedings AMLTA-2012), Communications in Computer and Information Science (CCIS) (Vol. 322, pp. 82–91). Berlin: Springer.

2. Abdelbar, A. M., & Salama, K. M. (2016). Clustering with the ACO $${_{\mathbb{R}}}$$ R algorithm. In: M. Dorigo, M. Birattari, X. Li, M. López-Ibáñez, K. Ohkura, C. Pinciroli & T. Stützle (Eds.), Swarm intelligence (Proceedings ANTS-2016), Lecture Notes in Computer Science (Vol. 9882, pp. 291–292). Berlin: Springer.

3. Arthur, D., & Vassilvitskii, S. (2007). k-means $$++$$ + + : The advantages of careful seeding. In Proceedings ACM-SIAM Symposium on Discrete Algorithms (SODA-2007) (pp. 1027–1035). Philadelphia, PA: Society for Industrial and Applied Mathematics.

4. Ashok, L., & Messinger, D. (2012). A spectral image clustering algorithm based on ant colony optimization. In Proceedings SPIE 8390, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVIII (p. 83901P). SPIE Press.

5. Asuncion, A., & Newman, D. (2007). UCI Machine Learning Repository. http://www.ics.uci.edu/~mlearn/MLRepository.html .

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