Abstract
The harmony search (HS) is a music-inspired algorithm that appeared in the year 2001. Since its introduction HS has undergone a lot of changes and has been applied to diverse disciplines. The aim of this paper is to inform readers about the HS applications in data mining. The review is expected to provide an outlook on the use of HS in data mining, especially for those researchers who are keen to explore the algorithm’s capabilities in data mining.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Geem, Z.W., Kim, J.H., Loganathan, G.V.: A new heuristic optimization algorithm harmony search. Simulation 76(2), 60–68 (2001)
Lee, K.S., Geem, Z.W.: A new structural optimization method based on the harmony search algorithm. Comput. Struct. 82(9–10), 781–798 (2004)
Geem, Z.W., Lee, K.S., Park, Y.: Application of harmony search to vehicle routing. Am. J. Appl. Sci. 2(12), 1552–1557 (2005)
Geem, Z.W., Hwangbo, H.: Application of harmony search to multi-objective optimization for satellite heat pipe design. In: Proceedings of, pp. 1–3 (2006, August)
Wang, Y., Liu, Y., Feng, L., Zhu, X.: Novel feature selection method based on harmony search for email classification. Knowl.-Based Syst. 73, 311–323 (2015)
Huang, Y.F., Lin, S.M., Wu, H.Y., Li, Y.S.: Music genre classification based on local feature selection using a self-adaptive harmony search algorithm. Data Knowl. Eng. 92, 60–76 (2014)
Wang, X., Gao, X.Z., Ovaska, S.J.: Fusion of clonal selection algorithm and harmony search method in optimisation of fuzzy classification systems. Int. J. Bio-Inspired Comput. 1(1–2), 80–88 (2009)
Montana, D.J., Davis, L.: Training feedforward neural networks using genetic algorithms. IJCAI 89, 762–767 (1989)
Leung, F.H., Lam, H.K., Ling, S.H., Tam, P.K.: Tuning of the structure and parameters of a neural network using an improved genetic algorithm. IEEE Trans. Neural Netw. 14(1), 79–88 (2003)
Mendes, R., Cortez, P., Rocha, M., Neves, J.: Particle swarms for feedforward neural network training. Learning 6(1), (2002)
Ilonen, J., Kamarainen, J.K., Lampinen, J.: Differential evolution training algorithm for feed-forward neural networks. Neural Process. Lett. 17(1), 93–105 (2003)
Socha, K., Blum, C.: An ant colony optimization algorithm for continuous optimization: application to feed-forward neural network training. Neural Comput. Appl. 16(3), 235–247 (2007)
Blum, C., Socha, K.: Training feed-forward neural networks with ant colony optimization: An application to pattern classification. In: Fifth International Conference on Hybrid Intelligent Systems, 2005. HIS’05. (pp. 6–pp). IEEE (2005, November)
Kulluk, S., Ozbakir, L., Baykasoglu, A.: Training neural networks with harmony search algorithms for classification problems. Eng. Appl. Artif. Intell. 25(1), 11–19 (2012)
Kulluk, S., Ozbakir, L., Baykasoglu, A.: Self-adaptive global best harmony search algorithm for training neural networks. Procedia Comput. Sci. 3, 282–286 (2011)
Sheen, S., Anitha, R., Sirisha, P.: Malware detection by pruning of parallel ensembles using harmony search. Pattern Recogn. Lett. 34(14), 1679–1686 (2013)
Alexandre, E., Cuadra, L., Gil-Pita, R.: Sound classification in hearing aids by the harmony search algorithm. In: Music-Inspired Harmony Search Algorithm, pp. 173–188. Springer, Berlin (2009)
Amiri, B., Hossain, L., Mosavi, S.E.: Application of harmony search algorithm on clustering. In: Proceedings of the World Congress on Engineering and Computer Science, vol. 1, pp. 20–22, (2010, October)
Mahdavi, M., Abolhassani, H.: Harmony K-means algorithm for document clustering. Data Min. Knowl. Disc. 18(3), 370–391 (2009)
Cobos, C., Andrade, J., Constain, W., Mendoza, M., León, E.: Web document clustering based on global-best harmony search, K-means, frequent term sets and Bayesian information criterion. In: 2010 IEEE Congress on Evolutionary Computation (CEC), pp. 1–8. IEEE (2010, July)
Moh’d Alia, O., Al-Betar, M.A., Mandava, R., Khader, A.T.: Data clustering using harmony search algorithm. In: Swarm, Evolutionary, and Memetic Computing, pp. 79–88. Springer, Berlin (2011)
Kumar, V., Chhabra, J.K., Kumar, D.: Clustering using modified harmony search algorithm. Int. J. Comput. Intell. Stud. 2, 3(2–3), 113–133 (2014)
Ibtissem, B., Hadria, F.: Unsupervised clustering of images using harmony search algorithm. Nature 1(5), 91–99 (2013)
Forsati, R., Mahdavi, M., Kangavari, M., Safarkhani, B.: Web page clustering using harmony search optimization. In: Canadian Conference on Electrical and Computer Engineering, 2008. CCECE 2008, pp. 001601–001604, IEEE (2008, May)
Forsati, R., Meybodi, M., Mahdavi, M., Neiat, A.: Hybridization of K-means and harmony search methods for web page clustering. In: Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, vol. 01, pp. 329–335, IEEE Computer Society (2008, December)
Hoang, D.C., Yadav, P., Kumar, R., Panda, S.K.: A robust harmony search algorithm based clustering protocol for wireless sensor networks. In: 2010 IEEE International Conference on Communications Workshops (ICC), pp. 1–5, IEEE (2010, May)
Ramos, C.C., Souza, A.N., Chiachia, G., Falcão, A.X., Papa, J.P.: A novel algorithm for feature selection using harmony search and its application for non-technical losses detection. Comput. Electr. Eng. 37(6), 886–894 (2011)
Diao, R., Shen, Q.: Feature selection with harmony search. IEEE Trans. Syst. Man Cybern. B Cybern. 42(6), 1509–1523 (2012)
Diao, R., Shen, Q.: Two new approaches to feature selection with harmony search. In: 2010 IEEE International Conference on Fuzzy Systems (FUZZ), pp. 1–7, IEEE (2010, July)
Wong, W.K., Guo, Z.X.: A hybrid intelligent model for medium-term sales forecasting in fashion retail supply chains using extreme learning machine and harmony search algorithm. Int. J. Prod. Econ. 128(2), 614–624 (2010)
Dash, R., Dash, P.K., Bisoi, R.: A differential harmony search based hybrid interval type 2 fuzzy EGARCH model for stock market volatility prediction. Int. J. Approximate Reasoning 59, 81–104 (2015)
Razfar, M.R., Zinati, R.F., Haghshenas, M.: Optimum surface roughness prediction in face milling by using neural network and harmony search algorithm. Int. J. Adv. Manuf. Technol. 52(5–8), 487–495 (2011)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer Science+Business Media Singapore
About this paper
Cite this paper
Assif Assad, Deep, K. (2016). Applications of Harmony Search Algorithm in Data Mining: A Survey. In: Pant, M., Deep, K., Bansal, J., Nagar, A., Das, K. (eds) Proceedings of Fifth International Conference on Soft Computing for Problem Solving. Advances in Intelligent Systems and Computing, vol 437. Springer, Singapore. https://doi.org/10.1007/978-981-10-0451-3_77
Download citation
DOI: https://doi.org/10.1007/978-981-10-0451-3_77
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-0450-6
Online ISBN: 978-981-10-0451-3
eBook Packages: EngineeringEngineering (R0)