Development of New Clustering Algorithm Based on Firefly Optimization

Year 2019, Volume: 2 Issue: 2, 21 - 26, 28.12.2019

Mino Alabd Alrahman Hasan Erdinç Koçer

Abstract

Clustering is a grouping of data with similar characteristics in a data set. Within the same cluster, the similarities are high and the similarities between the clusters are low. Clustering algorithms often have unsupervised learning, so no prior information is given. In this article, firefly optimization algorithm has been applied to find the optimum cluster centers. This algorithm has a global search capability and generally is used to solve difficult problems. The proposed clustering algorithm was testedon 12 data sets from UCI data warehouse. For evaluation of performance of this new approach, the proposed clustering algorithm are compared with twelve other clustering algorithms (SFLA, ABC, PSO, Bayes Net, Mlp ANN, RBF, KStar, Bagging, Multi Boost, NB Tree, Ridor and VFI). As a result of this study, the proposed approach has performed better than many clustering algorithms in many dataset

Keywords

Clustering, clustering by firefly, clustering methods, firefly optimization.

References

• [1] Gulati, H. and Singh, P., 2015, "Clustering techniques in data mining: A comparison," Computing for Sustainable Global Development (INDIACom), 2015 2nd International Conference on, pp. 410-415.
• [2] Dekhici, L., Borne, P. and Khaled, B., 2012, "Firefly algorithm for economic power dispatching with pollutants emission," Informatica Economică, vol. 16, no. 2, pp. 45-57.
• [3] Yang, X., S., and He, X., 2013, "Firefly algorithm: recent advances and applications," International Journal of Swarm Intelligence, vol. 1, no. 1, pp. 36-50.
• [4] Aydilek, İ. B., 2017, "Değiştirilmiş ateşböceği optimizasyon algoritması ile kural tabanlı çoklu sınıflama yapılması," Gazi Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi, vol. 32, no. 4.
• [5] Pal, S. K., Rai, C. and Singh, A. P., 2012, "Comparative study of firefly algorithm and particle swarm optimization for noisy non-linear optimization problems," International Journal of Intelligent Systems and Applications, vol. 4, no. 10, p. 50.
• [6] Gandomi, A. H., Yang, X.-S. and Alavi, A. H., 2011, "Mixed variable structural optimization using firefly algorithm," Computers & Structures, vol. 89, no. 23, pp. 2325-2336.
• [7] Wang, H., Wang, W., Zhou, X., Sun, H., Zhao, J., Yu, X., & Cui, Z., 2017, "Firefly algorithm with neighborhood attraction," Information Sciences, vol. 382, pp. 374-387.
• [8] Yang, X.-S., 2013, "Multiobjective firefly algorithm for continuous optimization," Engineering with Computers, vol. 29, no. 2, pp. 175-184.
• [9] Khadwilard, A., Chansombat, S., Thepphakorn, T., Thapatsuwan, P., Chainate, W., and Pongcharoen, P., 2012, "Application of firefly algorithm and its parameter setting for job shop scheduling," J. Ind. Technol, vol. 8, no. 1.
• [10] Karakoyun, M., and Babalik, A., 2015, "Data Clustering with Shuffled Leaping Frog Algorithm (SFLA) for Classification," 2015 Int'l Conference on Intelligent Computing, Electronics Systems and Information Technology (ICESIT-15).
• [11] Umbarkar, A. J., Balande, U. T., & Seth, P. D., 2017, "Performance evaluation of firefly algorithm with variation in sorting for non-linear benchmark problems," In AIP Conference Proceedings, vol. 1836, No. 1, p. 020032, AIP Publishing.
• [12] Akşehirli, M. E., 2011, "New cluster ensemble algorithm with automatic cluster number and new pruning technique for fast detection of neighbors on binary data," Master of Science Thesis, Bahçeşehir Üniversitesi, Istanbul.
• [13] Avcı, U., 2006, "Bulanık kümeleme algoritmalarının karşılaştırmalı analizi ve bilgisayar uygulamaları," Ege Üniversitesi.
• [14] Güler, N., 2006, "Bulanık Kümeleme Analizi ve Bulanık Modellemeye Uygulamaları," Yüksek Lisans Tezi, Muğla Üniversitesi Fen Bilimleri Enstitüsü, Muğla.
• [15] Akyol, S. and Alataş, B., 2012, "Güncel Sürü Zekası Optamizasyon Algoritmaları," Nevşehir Bilim ve Teknoloji Dergisi, vol. 1, no. 1.
• [16] Francisco, R. B., Costa, M. F. P., and Rocha, A. M. A., 2014, "Experiments with Firefly Algorithm," In International Conference on Computational Science and Its Applications (pp. 227-236). Springer, Cham.
• [17] Yang, X.-S., 2009, "Firefly algorithms for multimodal optimization," International symposium on stochastic algorithms, pp. 169-178.
• [18] Apostolopoulos, T. and Vlachos, A., 2010, "Application of the firefly algorithm for solving the economic emissions load dispatch problem," International Journal of Combinatorics, vol. 2011.
• [19] Ritthipakdee, A., Thammano, A., Premasathian, N. and Uyyanonvara, B., 2014, "An Improved Firefly Algorithm for Optimization Problems," ADCONP, Hiroshima, (2), pp. 159-164.
• [20] UCI Machine Learning Repository [online], https://archive.ics.uci.edu/ml/datasets.html.