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How to cite this paper
APA: Duaimi, M., Bsoul, Q & AL-Gburi, A. (2024). Multi-objective of wind-driven optimization as feature selection and clustering to enhance text clustering. International Journal of Data and Network Science, 8(3), 1985-1998.
Chicago/Turabian: Duaimi, M., Bsoul, Q & AL-Gburi, A. 2024. "Multi-objective of wind-driven optimization as feature selection and clustering to enhance text clustering." International Journal of Data and Network Science 8, no. 3 (2024): 1985-1998.
AMA: Duaimi, M., Bsoul, Q & AL-Gburi, A. Multi-objective of wind-driven optimization as feature selection and clustering to enhance text clustering. International Journal of Data and Network Science. 2024;8(3):1985-1998.
References
Abualigah, L. M., & Khader, A. T. (2017). Unsupervised text feature selection technique based on hybrid particle swarm optimization algorithm with genetic operators for the text clustering. The Journal of Supercomputing, 73, 4773-4795.
Abualigah, L. M., Khader, A. T., & Al-Betar, M. A. (2016, July). Unsupervised feature selection technique based on genet-ic algorithm for improving the text clustering. In 2016 7th international conference on computer science and infor-mation technology (CSIT) (pp. 1-6). IEEE.
Abualigah, L. M., Khader, A. T., & Hanandeh, E. S. (2018). A new feature selection method to improve the document clustering using particle swarm optimization algorithm. Journal of Computational Science, 25, 456-466.
Abualigah, L. M., Khader, A. T., AlBetar, M. A., & Hanandeh, E. S. (2017, February). A new hybridization strategy for krill herd algorithm and harmony search algorithm applied to improve the data clustering. In First EAI international conference on computer science and engineering (pp. 54-63).
Akay, B., & Karaboga, D. (2009). Parameter tuning for the artificial bee colony algorithm. In Computational Collective Intelligence. Semantic Web, Social Networks and Multiagent Systems: First International Conference, ICCCI 2009, Wrocław, Poland, October 5-7, 2009. Proceedings 1 (pp. 608-619). Springer, Berlin.
Anitha, P., & Patil, M. M. (2022). RFM model for customer purchase behavior using K-Means algorithm. Journal of King Saud University-Computer and Information Sciences, 34(5), 1785-1792.
Bação, F., Lobo, V., & Painho, M. (2005). Self-organizing maps as substitutes for k-means clustering. In Computational Science–ICCS 2005: 5th International Conference, Atlanta, GA, USA, May 22-25, 2005, Proceedings, Part III 5 (pp. 476-483). Springer Berlin Heidelberg.
Bayraktar, Z., Komurcu, M., & Werner, D. H. (2010, July). Wind Driven Optimization (WDO): A novel nature-inspired optimization algorithm and its application to electromagnetics. In 2010 IEEE antennas and propagation society inter-national symposium (pp. 1-4). IEEE.
Bharti, K. K., & Singh, P. K. (2014). A three-stage unsupervised dimension reduction method for text clustering. Journal of Computational Science, 5(2), 156-169.
Bouras, C., & Tsogkas, V. (2010, May). Assigning web news to clusters. In 2010 Fifth International Conference on Inter-net and Web Applications and Services (pp. 1-6). IEEE.
Brahimi, B., Touahria, M., & Tari, A. (2021). Improving sentiment analysis in Arabic: A combined approach. Journal of King Saud University-Computer and Information Sciences, 33(10), 1242-1250.
Bsoul, Q., Al-Shamari, E., Mohd, M., & Atwan, J. (2014). Distance measures and stemming impact on arabic document clustering. In Information Retrieval Technology: 10th Asia Information Retrieval Societies Conference, AIRS 2014, Kuching, Malaysia, December 3-5, 2014. Proceedings 10 (pp. 327-339). Springer International Publishing.
Bsoul, Q., Salim, J., & Zakaria, L. Q. (2013). An intelligent document clustering approach to detect crime patterns. Proce-dia Technology, 11, 1181-1187.
Cagnina, L., Errecalde, M., Ingaramo, D., & Rosso, P. (2014). An efficient particle swarm optimization approach to clus-ter short texts. Information Sciences, 265, 36-49.
Common IR Test Collection (2010). http://web.eecs.utk.edu/research/lsi/corpa.html
Dai, X. Y., Chen, Q. C., Wang, X. L., & Xu, J. (2010, July). Online topic detection and tracking of financial news based on hierarchical clustering. In 2010 International Conference on Machine Learning and Cybernetics (Vol. 6, pp. 3341-3346). IEEE.
Dai, X., He, Y., & Sun, Y. (2010, October). A two-layer text clustering approach for retrospective news event detection. In 2010 International Conference on Artificial Intelligence and Computational Intelligence (Vol. 1, pp. 364-368). IEEE.
Devi, S. S., Shanmugam, A., & Prabha, E. D. (2015). A proficient method for text clustering using harmony search meth-od. International Journal of Scientific Research in Science, Engineering and Technology, 1, 145-150.
Dueck, D., & Frey, B. J. (2007, October). Non-metric affinity propagation for unsupervised image categorization. In 2007 IEEE 11th international conference on computer vision (pp. 1-8). IEEE.
Forsati, R., Mahdavi, M., Shamsfard, M., & Meybodi, M. R. (2013). Efficient stochastic algorithms for document cluster-ing. Information Sciences, 220, 269-291.
Gbadoubissa, J. E. Z., Ari, A. A. A., & Gueroui, A. M. (2020). Efficient k-means based clustering scheme for mobile net-works cell sites management. Journal of King Saud University-Computer and Information Sciences, 32(9), 1063-1070.
Handl, J., & Knowles, J. (2007). An evolutionary approach to multiobjective clustering. IEEE transactions on Evolution-ary Computation, 11(1), 56-76.
Hartigan, J. A. (1981). Consistency of single linkage for high-density clusters. Journal of the American Statistical Associ-ation, 76(374), 388-394.
Jain, A. K., Murty, M. N., & Flynn, P. J. (1999). Data clustering: a review. ACM computing surveys (CSUR), 31(3), 264-323.
Jardine, N., & van Rijsbergen, C. J. (1971). The use of hierarchic clustering in information retrieval. Information storage and retrieval, 7(5), 217-240.
Jo, T. (2009, July). Clustering news groups using inverted index based NTSO. In 2009 First International Conference on Networked Digital Technologies (pp. 1-7). IEEE.
Labani, M., Moradi, P., Ahmadizar, F., & Jalili, M. (2018). A novel multivariate filter method for feature selection in text classification problems. Engineering Applications of Artificial Intelligence, 70, 25-37.
Lang, K. (2008). The 20 news groups data set. http://people. csail. mit. edu/jrennie/20Newsgroups/.
Larsen, B., & Aone, C. (1999, August). Fast and effective text mining using linear-time document clustering. In Proceed-ings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 16-22).
Lewis, D. D. (1997). Test Collections: Reuters-21578. URL: http://www. daviddlewis. com/resources/testcollections/reuters21578.
Manoj, V. J., & Elias, E. (2012). Artificial bee colony algorithm for the design of multiplier-less nonuniform filter bank transmultiplexer. Information Sciences, 192, 193-203.
Mafarja, M. M., & Mirjalili, S. (2017). Hybrid whale optimization algorithm with simulated annealing for feature selec-tion. Neurocomputing, 260, 302-312.
Mehra, P. S., Doja, M. N., & Alam, B. (2020). Fuzzy based enhanced cluster head selection (FBECS) for WSN. Journal of King Saud University-Science, 32(1), 390-401.
Mohd, M., Crestani, F., & Ruthven, I. (2012). Evaluation of an interactive topic detection and tracking interface. Journal of information science, 38(4), 383-398.
Qasim, I., Jeong, J. W., Heu, J. U., & Lee, D. H. (2013). Concept map construction from text documents using affinity propagation. Journal of Information Science, 39(6), 719-736.
Romeo, S., Da San Martino, G., Belinkov, Y., Barrón-Cedeño, A., Eldesouki, M., Darwish, K., ... & Moschitti, A. (2019). Language processing and learning models for community question answering in arabic. Information Processing & Management, 56(2), 274-290.
Sahmoudi, I., & Lachkar, A. (2017). Formal concept analysis for Arabic web search results clustering. Journal of King Saud University-Computer and Information Sciences, 29(2), 196-203.
Selim, S. Z., & Ismail, M. A. (1984). K-means-type algorithms: A generalized convergence theorem and characterization of local optimality. IEEE Transactions on pattern analysis and machine intelligence, 1, 81-87.
Tabakhi, S., Moradi, P., & Akhlaghian, F. (2014). An unsupervised feature selection algorithm based on ant colony opti-mization. Engineering Applications of Artificial Intelligence, 32, 112-123.
Velmurugan, T., & Santhanam, T. (2011). A survey of partition based clustering algorithms in data mining: An experi-mental approach. Information Technology Journal, 10(3), 478-484.
Wan, Y., Zhong, Y., & Ma, A. (2018). Fully automatic spectral–spatial fuzzy clustering using an adaptive multiobjective memetic algorithm for multispectral imagery. IEEE Transactions on Geoscience and Remote Sensing, 57(4), 2324-2340.
Wu, J., Dong, M., Ota, K., Li, J., & Guan, Z. (2018). Big data analysis-based secure cluster management for optimized control plane in software-defined networks. IEEE Transactions on Network and Service Management, 15(1), 27-38.
Yang, F., Sun, T., & Zhang, C. (2009). An efficient hybrid data clustering method based on K-harmonic means and Parti-cle Swarm Optimization. Expert Systems with Applications, 36(6), 9847-9852.
Zhang, Y., Li, H. G., Wang, Q., & Peng, C. (2019). A filter-based bare-bone particle swarm optimization algorithm for un-supervised feature selection. Applied Intelligence, 49, 2889-2898.
Zhe, G., Dan, L., Baoyu, A., Yangxi, O., Wei, C., Xinxin, N., & Yang, X. (2011). An analysis of ant colony clustering methods: Models, algorithms and applications. International Journal of Advancements in Computing Technology, 3(11), 112-121.
Abualigah, L. M., Khader, A. T., & Al-Betar, M. A. (2016, July). Unsupervised feature selection technique based on genet-ic algorithm for improving the text clustering. In 2016 7th international conference on computer science and infor-mation technology (CSIT) (pp. 1-6). IEEE.
Abualigah, L. M., Khader, A. T., & Hanandeh, E. S. (2018). A new feature selection method to improve the document clustering using particle swarm optimization algorithm. Journal of Computational Science, 25, 456-466.
Abualigah, L. M., Khader, A. T., AlBetar, M. A., & Hanandeh, E. S. (2017, February). A new hybridization strategy for krill herd algorithm and harmony search algorithm applied to improve the data clustering. In First EAI international conference on computer science and engineering (pp. 54-63).
Akay, B., & Karaboga, D. (2009). Parameter tuning for the artificial bee colony algorithm. In Computational Collective Intelligence. Semantic Web, Social Networks and Multiagent Systems: First International Conference, ICCCI 2009, Wrocław, Poland, October 5-7, 2009. Proceedings 1 (pp. 608-619). Springer, Berlin.
Anitha, P., & Patil, M. M. (2022). RFM model for customer purchase behavior using K-Means algorithm. Journal of King Saud University-Computer and Information Sciences, 34(5), 1785-1792.
Bação, F., Lobo, V., & Painho, M. (2005). Self-organizing maps as substitutes for k-means clustering. In Computational Science–ICCS 2005: 5th International Conference, Atlanta, GA, USA, May 22-25, 2005, Proceedings, Part III 5 (pp. 476-483). Springer Berlin Heidelberg.
Bayraktar, Z., Komurcu, M., & Werner, D. H. (2010, July). Wind Driven Optimization (WDO): A novel nature-inspired optimization algorithm and its application to electromagnetics. In 2010 IEEE antennas and propagation society inter-national symposium (pp. 1-4). IEEE.
Bharti, K. K., & Singh, P. K. (2014). A three-stage unsupervised dimension reduction method for text clustering. Journal of Computational Science, 5(2), 156-169.
Bouras, C., & Tsogkas, V. (2010, May). Assigning web news to clusters. In 2010 Fifth International Conference on Inter-net and Web Applications and Services (pp. 1-6). IEEE.
Brahimi, B., Touahria, M., & Tari, A. (2021). Improving sentiment analysis in Arabic: A combined approach. Journal of King Saud University-Computer and Information Sciences, 33(10), 1242-1250.
Bsoul, Q., Al-Shamari, E., Mohd, M., & Atwan, J. (2014). Distance measures and stemming impact on arabic document clustering. In Information Retrieval Technology: 10th Asia Information Retrieval Societies Conference, AIRS 2014, Kuching, Malaysia, December 3-5, 2014. Proceedings 10 (pp. 327-339). Springer International Publishing.
Bsoul, Q., Salim, J., & Zakaria, L. Q. (2013). An intelligent document clustering approach to detect crime patterns. Proce-dia Technology, 11, 1181-1187.
Cagnina, L., Errecalde, M., Ingaramo, D., & Rosso, P. (2014). An efficient particle swarm optimization approach to clus-ter short texts. Information Sciences, 265, 36-49.
Common IR Test Collection (2010). http://web.eecs.utk.edu/research/lsi/corpa.html
Dai, X. Y., Chen, Q. C., Wang, X. L., & Xu, J. (2010, July). Online topic detection and tracking of financial news based on hierarchical clustering. In 2010 International Conference on Machine Learning and Cybernetics (Vol. 6, pp. 3341-3346). IEEE.
Dai, X., He, Y., & Sun, Y. (2010, October). A two-layer text clustering approach for retrospective news event detection. In 2010 International Conference on Artificial Intelligence and Computational Intelligence (Vol. 1, pp. 364-368). IEEE.
Devi, S. S., Shanmugam, A., & Prabha, E. D. (2015). A proficient method for text clustering using harmony search meth-od. International Journal of Scientific Research in Science, Engineering and Technology, 1, 145-150.
Dueck, D., & Frey, B. J. (2007, October). Non-metric affinity propagation for unsupervised image categorization. In 2007 IEEE 11th international conference on computer vision (pp. 1-8). IEEE.
Forsati, R., Mahdavi, M., Shamsfard, M., & Meybodi, M. R. (2013). Efficient stochastic algorithms for document cluster-ing. Information Sciences, 220, 269-291.
Gbadoubissa, J. E. Z., Ari, A. A. A., & Gueroui, A. M. (2020). Efficient k-means based clustering scheme for mobile net-works cell sites management. Journal of King Saud University-Computer and Information Sciences, 32(9), 1063-1070.
Handl, J., & Knowles, J. (2007). An evolutionary approach to multiobjective clustering. IEEE transactions on Evolution-ary Computation, 11(1), 56-76.
Hartigan, J. A. (1981). Consistency of single linkage for high-density clusters. Journal of the American Statistical Associ-ation, 76(374), 388-394.
Jain, A. K., Murty, M. N., & Flynn, P. J. (1999). Data clustering: a review. ACM computing surveys (CSUR), 31(3), 264-323.
Jardine, N., & van Rijsbergen, C. J. (1971). The use of hierarchic clustering in information retrieval. Information storage and retrieval, 7(5), 217-240.
Jo, T. (2009, July). Clustering news groups using inverted index based NTSO. In 2009 First International Conference on Networked Digital Technologies (pp. 1-7). IEEE.
Labani, M., Moradi, P., Ahmadizar, F., & Jalili, M. (2018). A novel multivariate filter method for feature selection in text classification problems. Engineering Applications of Artificial Intelligence, 70, 25-37.
Lang, K. (2008). The 20 news groups data set. http://people. csail. mit. edu/jrennie/20Newsgroups/.
Larsen, B., & Aone, C. (1999, August). Fast and effective text mining using linear-time document clustering. In Proceed-ings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 16-22).
Lewis, D. D. (1997). Test Collections: Reuters-21578. URL: http://www. daviddlewis. com/resources/testcollections/reuters21578.
Manoj, V. J., & Elias, E. (2012). Artificial bee colony algorithm for the design of multiplier-less nonuniform filter bank transmultiplexer. Information Sciences, 192, 193-203.
Mafarja, M. M., & Mirjalili, S. (2017). Hybrid whale optimization algorithm with simulated annealing for feature selec-tion. Neurocomputing, 260, 302-312.
Mehra, P. S., Doja, M. N., & Alam, B. (2020). Fuzzy based enhanced cluster head selection (FBECS) for WSN. Journal of King Saud University-Science, 32(1), 390-401.
Mohd, M., Crestani, F., & Ruthven, I. (2012). Evaluation of an interactive topic detection and tracking interface. Journal of information science, 38(4), 383-398.
Qasim, I., Jeong, J. W., Heu, J. U., & Lee, D. H. (2013). Concept map construction from text documents using affinity propagation. Journal of Information Science, 39(6), 719-736.
Romeo, S., Da San Martino, G., Belinkov, Y., Barrón-Cedeño, A., Eldesouki, M., Darwish, K., ... & Moschitti, A. (2019). Language processing and learning models for community question answering in arabic. Information Processing & Management, 56(2), 274-290.
Sahmoudi, I., & Lachkar, A. (2017). Formal concept analysis for Arabic web search results clustering. Journal of King Saud University-Computer and Information Sciences, 29(2), 196-203.
Selim, S. Z., & Ismail, M. A. (1984). K-means-type algorithms: A generalized convergence theorem and characterization of local optimality. IEEE Transactions on pattern analysis and machine intelligence, 1, 81-87.
Tabakhi, S., Moradi, P., & Akhlaghian, F. (2014). An unsupervised feature selection algorithm based on ant colony opti-mization. Engineering Applications of Artificial Intelligence, 32, 112-123.
Velmurugan, T., & Santhanam, T. (2011). A survey of partition based clustering algorithms in data mining: An experi-mental approach. Information Technology Journal, 10(3), 478-484.
Wan, Y., Zhong, Y., & Ma, A. (2018). Fully automatic spectral–spatial fuzzy clustering using an adaptive multiobjective memetic algorithm for multispectral imagery. IEEE Transactions on Geoscience and Remote Sensing, 57(4), 2324-2340.
Wu, J., Dong, M., Ota, K., Li, J., & Guan, Z. (2018). Big data analysis-based secure cluster management for optimized control plane in software-defined networks. IEEE Transactions on Network and Service Management, 15(1), 27-38.
Yang, F., Sun, T., & Zhang, C. (2009). An efficient hybrid data clustering method based on K-harmonic means and Parti-cle Swarm Optimization. Expert Systems with Applications, 36(6), 9847-9852.
Zhang, Y., Li, H. G., Wang, Q., & Peng, C. (2019). A filter-based bare-bone particle swarm optimization algorithm for un-supervised feature selection. Applied Intelligence, 49, 2889-2898.
Zhe, G., Dan, L., Baoyu, A., Yangxi, O., Wei, C., Xinxin, N., & Yang, X. (2011). An analysis of ant colony clustering methods: Models, algorithms and applications. International Journal of Advancements in Computing Technology, 3(11), 112-121.