Data Clustering Using Harmony Search Algorithm

  • Osama Moh’d Alia
  • Mohammed Azmi Al-Betar
  • Rajeswari Mandava
  • Ahamad Tajudin Khader
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7077)


Being one of the main challenges to clustering algorithms, the sensitivity of fuzzy c-means (FCM) and hard c-means (HCM) to tune the initial clusters centers has captured the attention of the clustering communities for quite a long time. In this study, the new evolutionary algorithm, Harmony Search (HS), is proposed as a new method aimed at addressing this problem. The proposed approach consists of two stages. In the first stage, the HS explores the search space of the given dataset to find out the near-optimal cluster centers. The cluster centers found by the HS are then evaluated using reformulated c-means objective function. In the second stage, the best cluster centers found are used as the initial cluster centers for the c-means algorithms. Our experiments show that an HS can minimize the difficulty of choosing an initialization for the c-means clustering algorithms. For purposes of evaluation, standard benchmark data are experimented with, including the Iris, BUPA liver disorders, Glass, Diabetes, etc. along with two generated data that have several local extrema.


Cluster Center Harmony Search Harmony Search Algorithm Harmony Memory Artificial Dataset 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Jain, A.K., Murty, M.N., Flynn, P.J.: Data clustering: a review. ACM Comput. Surv. 31(3), 264–323 (1999)CrossRefGoogle Scholar
  2. 2.
    Hathaway, R.J., Bezdek, J.C.: Local convergence of the fuzzy c-means algorithms. Pattern Recognition 19(6), 477–480 (1986)CrossRefzbMATHGoogle Scholar
  3. 3.
    Kanade, P.M., Hall, L.O.: Fuzzy ants and clustering. IEEE Transactions on Systems, Man and Cybernetics, Part A 37(5), 758–769 (2007)CrossRefGoogle Scholar
  4. 4.
    Selim, S.Z., Alsultan, K.: A simulated annealing algorithm for the clustering problem. Pattern Recognition 24(10), 1003–1008 (1991)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Al-Sultan, K.S.: A tabu search approach to the clustering problem. Pattern Recognition 28(9), 1443–1451 (1995)CrossRefGoogle Scholar
  6. 6.
    Hall, L.O., Ozyurt, I.B., Bezdek, J.C.: Clustering with a genetically optimized approach. IEEE Transactions on Evolutionary Computation 3(2), 103–112 (1999)CrossRefGoogle Scholar
  7. 7.
    Bandyopadhyay, S., Maulik, U.: An evolutionary technique based on k-means algorithm for optimal clustering in rn. Information Sciences 146(1-4), 221–237 (2002)MathSciNetCrossRefzbMATHGoogle Scholar
  8. 8.
    Lili, L., Xiyu, L., Mingming, X.: A novel fuzzy clustering based on particle swarm optimization. In: First IEEE International Symposium on Information Technologies and Applications in Education, ISITAE, pp. 88–90 (2007)Google Scholar
  9. 9.
    Maulik, U., Saha, I.: Modified differential evolution based fuzzy clustering for pixel classification in remote sensing imagery. Pattern Recognition 42(9), 2135–2149 (2009)CrossRefzbMATHGoogle Scholar
  10. 10.
    Paterlini, S., Krink, T.: Differential evolution and particle swarm optimisation in partitional clustering. Computational Statistics & Data Analysis 50(5), 1220–1247 (2006)MathSciNetCrossRefzbMATHGoogle Scholar
  11. 11.
    Geem, Z.W., Kim, J.H., Loganathan, G.: A new heuristic optimization algorithm: harmony search. Simulation 76(2), 60–68 (2001)CrossRefGoogle Scholar
  12. 12.
    Alia, O., Mandava, R.: The variants of the harmony search algorithm: an overview. Artificial Intelligence Review 36, 49–68 (2011), 10.1007/s10462-010-9201-yCrossRefGoogle Scholar
  13. 13.
    Geem, Z.W.: Music-inspired Harmony Search Algorithm Theory and Applications. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  14. 14.
    Ayvaz, M.T.: Simultaneous determination of aquifer parameters and zone structures with fuzzy c-means clustering and meta-heuristic harmony search algorithm. Advances in Water Resources 30(11), 2326–2338 (2007)CrossRefGoogle Scholar
  15. 15.
    Mahdavi, M., Chehreghani, M.H., Abolhassani, H., Forsati, R.: Novel meta-heuristic algorithms for clustering web documents. Applied Mathematics and Computation 201(1-2), 441–451 (2008)MathSciNetCrossRefzbMATHGoogle Scholar
  16. 16.
    Wang, X., Gao, X.Z., Ovaska, S.J.: A hybrid optimization method for fuzzy classification systems. In: Eighth International Conference on Hybrid Intelligent Systems, HIS 2008, pp. 264–271 (2008)Google Scholar
  17. 17.
    Malaki, M., Pourbaghery, J.A., Abolhassani, H.: A combinatory approach to fuzzy clustering with harmony search and its applications to space shuttle data. In: SCIS & ISIS 2008, Nagoya, Japan (2008)Google Scholar
  18. 18.
    Alia, O.M., Mandava, R., Aziz, M.E.: A hybrid harmony search algorithm to MRI brain segmentation. In: The 9th IEEE International Conference on Cognitive Informatics, ICCI 2010, pp. 712–719. IEEE, Tsinghua University (2010)Google Scholar
  19. 19.
    Forgy, E.: Cluster analysis of multivariate data: Efficiency vs. interpretability of classifications. Biometrics 21(3), 768 (1965)Google Scholar
  20. 20.
    Bezdek, J.C.: Pattern Recognition with Fuzzy Objective Function Algorithms. Kluwer Academic Publishers (1981)Google Scholar
  21. 21.
    Hathaway, R.J., Bezdek, J.C.: Optimization of clustering criteria by reformulation. IEEE Transactions on Fuzzy Systems 3(2), 241–245 (1995)CrossRefGoogle Scholar
  22. 22.
    Alata, M., Molhim, M., Ramini, A.: Optimizing of fuzzy c-means clustering algorithm using GA. Proceedings of World Academy of Science, Engineering and Technology 29 (2008)Google Scholar
  23. 23.
    Al-Betar, M., Khader, A.: A hybrid harmony search for university course timetabling. In: Proceedings of the 4nd Multidisciplinary Conference on scheduling: Theory and Applications (MISTA 2009), Dublin, Ireland, pp. 10–12 (August 2009)Google Scholar
  24. 24.
    Al-Betar, M., Khader, A.: A harmony search algorithm for university course timetabling. Annals of Operations Research, 1–29 (2008)Google Scholar
  25. 25.
    Al-Betar, M., Khader, A., Nadi, F.: Selection mechanisms in memory consideration for examination timetabling with harmony search. In: Proceedings of the 12th Annual Conference on Genetic and Evolutionary Computation, pp. 1203–1210. ACM (2010)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Osama Moh’d Alia
    • 1
  • Mohammed Azmi Al-Betar
    • 2
    • 3
  • Rajeswari Mandava
    • 2
  • Ahamad Tajudin Khader
    • 2
  1. 1.Faculty of Computing and Information TechnologyUniversity of TabukTabukKingdom of Saudi Arabia
  2. 2.School of Computer SciencesUniversiti Sains MalaysiaPenangMalaysia
  3. 3.School of Computer ScienceAL-Zaytoonah University of JordanAmmanMalaysia

Personalised recommendations