Abstract
Data clustering is an important activity in the field of data analytics. It can be described as unsupervised learning for grouping the similar objects into clusters. The similarity between objects is computed through distance measure. Further, clustering has proven its significance for solving wide range of real-world optimization problems. This work presents water wave optimization (WWO) based metaheuristic algorithm for clustering task. It is seen that WWO algorithm is an effective algorithm for solving constrained and unconstrained optimization problems. But, sometimes WWO cannot obtain promising solution for complex optimization problems due to absence of global best information component and converged on premature solution. To address the absentia of global best information and premature convergence, some improvements are inculcated in WWO algorithm to make it more promising and efficient. These improvements are described in terms of modified search mechanism and decay operator. The absentia of global best information component is handled through updated search mechanism. While, the premature convergence is addressed through a decay operator. The performance of WWO algorithm is evaluated using thirteen benchmark clustering datasets using accuracy and F-score parameters. The simulation results are compared with several state of art existing clustering algorithms and it is observed proposed WWO clustering algorithm achieves a higher accuracy and F-score rates with most of clustering datasets as compared to existing clustering algorithms. It is also showed that the proposed WWO algorithm improves the accuracy and F-score rates an average of 4% and 7% respectively as compared to existing clustering algorithm. Further, statistical test is also conducted to validate the existence of proposed WWO algorithm and statistical results confirm the existence of WWO algorithm in clustering field.
Similar content being viewed by others
References
Jain AK (2008) Data clustering: 50 years beyond k-means. In: Joint European conference on machine learning and knowledge discovery in databases. Springer, Berlin, Heidelberg, pp 3–4
Gong S, Hu W, Li H, Qu Y (2018) Property clustering in linked data: an empirical study and its application to entity browsing. Int J Semant Web Inf Syst (IJSWIS) 14(1):31–70
Chou CH, Hsieh SC, Qiu CJ (2017) Hybrid genetic algorithm and fuzzy clustering for bankruptcy prediction. Appl Soft Comput 56:298–316
Holý V, Sokol O, Černý M (2017) Clustering retail products based on customer behaviour. Appl Soft Comput 60:752–762
Navarro ÁAM, Ger PM (2018) Comparison of clustering algorithms for learning analytics with educational datasets. IJIMAI 5(2):9–16
Hyde R, Angelov P, MacKenzie AR (2017) Fully online clustering of evolving data streams into arbitrarily shaped clusters. Inf Sci 382:96–114
Wang L, Zhou X, Xing Y, Yang M, Zhang C (2017) Clustering ecg heartbeat using improved semi-supervised affinity propagation. IET Softw 11(5):207–213
Mekhmoukh A, Mokrani K (2015) Improved fuzzy C-means based particle swarm optimization (PSO) initialization and outlier rejection with level set methods for MR brain image segmentation. Comput Methods Prog Biomed 122(2):266–281
Abualigah LM, Khader AT, Al-Betar MA, Alomari OA (2017) Text feature selection with a robust weight scheme and dynamic dimension reduction to text document clustering. Expert Syst Appl 84:24–36
Triguero I, del Río S, López V, Bacardit J, Benítez JM, Herrera F (2015) ROSEFW-RF: the winner algorithm for the ECBDL’14 big data competition: an extremely imbalanced big data bioinformatics problem. Knowl-Based Syst 87:69–79
Zhu J, Lung CH, Srivastava V (2015) A hybrid clustering technique using quantitative and qualitative data for wireless sensor networks. Ad Hoc Netw 25:38–53
Abualigah LMQ (2019) Feature selection and enhanced krill herd algorithm for text document clustering. Springer, Berlin, pp 1–165
Marinakis Y, Marinaki M, Doumpos M, Zopounidis C (2009) Ant colony and particle swarm optimization for financial classification problems. Expert Syst Appl 36(7):10604–10611
Saraswathi S, Sheela MI (2014) A comparative study of various clustering algorithms in data mining. Int J Comput Sci Mob Comput 11(11):422–428
Hartigan JA, Wong MA (1979) Algorithm AS 136: a k-means clustering algorithm. J R Stat Soc Ser C Appl Stat 28(1):100–108
Celebi ME, Kingravi HA, Vela PA (2013) A comparative study of efficient initialization methods for the k-means clustering algorithm. Expert Syst Appl 40(1):200–210
Han J, Pei J, Kamber M (2011) Data mining: concepts and techniques. Elsevier, Amsterdam
Moreira A, Santos MY, Carneiro S (2005) Density-based clustering algorithms–DBSCAN and SNN. University of Minho-Portugal, pp 1–18
Schaeffer SE (2007) Graph clustering. Comput Sci Rev 1(1):27–64
Hufnagl B, Lohninger H (2020) A graph-based clustering method with special focus on hyperspectral imaging. Anal Chim Acta 1097:37–48
Nanda SJ, Panda G (2014) A survey on nature inspired metaheuristic algorithms for partitional clustering. Swarm Evol Comput 16:1–18
Nayyar A, Le DN, Nguyen NG (eds) (2018) Advances in swarm intelligence for optimizing problems in computer science. CRC Press, Boca Raton
Nayyar A, Nguyen NG (2018) Introduction to swarm intelligence. Adv Swarm Intell Optim Probl Comput Sci:53–78
Nayyar A, Garg S, Gupta D, Khanna A (2018) Evolutionary computation: theory and algorithms. In: Advances in swarm intelligence for optimizing problems in computer science. Chapman and Hall/CRC, pp 1–26
Sung CS, Jin HW (2000) A tabu-search-based heuristic for clustering. Pattern Recogn 33(5):849–858
Selim SZ, Alsultan K (1991) A simulated annealing algorithm for the clustering problem. Pattern Recogn 24(10):1003–1008
Maulik U, Bandyopadhyay S (2000) Genetic algorithm-based clustering technique. Pattern Recogn 33(9):1455–1465
Karaboga D, Ozturk C (2011) A novel clustering approach: artificial Bee Colony (ABC) algorithm. Appl Soft Comput 11(1):652–657
Sahoo G, Kumar Y (2017) A two-step artificial bee colony algorithm for clustering. Neural Comput Appl 28(3):537–551
Nayyar A, Puri V, Suseendran G (2019) Artificial bee Colony optimization—population-based meta-heuristic swarm intelligence technique. Data management, analytics and innovation. Springer, Singapore, pp 513–525
Kumar S, Nayyar A, Kumari R (2019) Arrhenius artificial bee colony algorithm. International conference on innovative computing and communications. Springer, Singapore, pp 187–195
Shelokar PS, Jayaraman VK, Kulkarni BD (2004) An ant colony approach for clustering. Anal Chim Acta 509(2):187–195
Nayyar A, Singh R (2016) Ant colony optimization—computational swarm intelligence technique. In: 2016 3rd International conference on computing for sustainable global development (INDIACom), IEEE, pp 1493–1499
Niknam T, Amiri B (2010) An efficient hybrid approach based on PSO, ACO and k-means for cluster analysis. Appl Soft Comput 10(1):183–197
Bouyer A, Hatamlou A (2018) An efficient hybrid clustering method based on improved cuckoo optimization and modified particle swarm optimization algorithms. Appl Soft Comput 67:172–182
Kumar Y, Singh PK (2018) Improved cat swarm optimization algorithm for solving global optimization problems and its application to clustering. Appl Intell 48(9):2681–2697
Kumar Y, Sahoo G (2015) A hybrid data clustering approach based on improved cat swarm optimization and K-harmonic mean algorithm. AI Commun 28(4):751–764
Senthilnath J, Omkar SN, Mani V (2011) Clustering using firefly algorithm: performance study. Swarm Evol Comput 1(3):164–171
Durbhaka GK, Selvaraj B, Nayyar A (2019) Firefly swarm: metaheuristic swarm intelligence technique for mathematical optimization. Data Management, Analytics and Innovation. Springer, Singapore, pp 457–466
Han X, Quan L, Xiong X, Almeter M, Xiang J, Lan Y (2017) A novel data clustering algorithm based on modified gravitational search algorithm. Eng Appl Artif Intell 61:1–7
Kumar Y, Sahoo G (2014) A review on gravitational search algorithm and its applications to data clustering & classification. Int J Intell Syst Appl 6(6):79
Hatamlou A (2013) Black hole: a new heuristic optimization approach for data clustering. Inf Sci 222:175–184
Kumar Y, Sahoo G (2014) A charged system search approach for data clustering. Prog Artif Intell 2(2–3):153–166
Kumar Y, Sahoo G (2015) Hybridization of magnetic charge system search and particle swarm optimization for efficient data clustering using neighborhood search strategy. Soft Comput 19(12):3621–3645
Kumar Y, Singh PK (2019) A chaotic teaching learning based optimization algorithm for clustering problems. Appl Intell 49(3):1036–1062
Singh H, Kumar Y, Kumar S (2019) A new meta-heuristic algorithm based on chemical reactions for partitional clustering problems. Evol Intel 12(2):241–252
Hatamlou A, Abdullah S, Hatamlou M (2011) Data clustering using big bang–big crunch algorithm. In: International conference on innovative computing technology. Springer, Berlin, Heidelberg, pp 383–388
Singh H, Kumar Y (2019) Hybrid big bang-big crunch algorithm for cluster analysis. In: International conference on futuristic trends in networks and computing technologies. Springer, Singapore, pp 648–661
Zhou Y, Wu H, Luo Q, Abdel-Baset M (2019) Automatic data clustering using nature-inspired symbiotic organism search algorithm. Knowl-Based Syst 163:546–557
Agbaje MB, Ezugwu AE, Els R (2019) Automatic data clustering using hybrid firefly particle swarm optimization algorithm. IEEE Access 7:184963–184984
Kushwaha N, Pant M, Sharma S (2019) Electromagnetic optimization‐based clustering algorithm. Expert Syst:e12491
Zhao F, Zhang L, Liu H, Zhang Y, Ma W, Zhang C, Song H (2019) An improved water wave optimization algorithm with the single wave mechanism for the no-wait flow-shop scheduling problem. Eng Optim 51(10):1727–1742
Singh G, Rattan M, Gill SS, Mittal N (2019) Hybridization of water wave optimization and sequential quadratic programming for cognitive radio system. Soft Comput 23(17):7991–8011
Zhao F, Liu H, Zhang Y, Ma W, Zhang C (2018) A discrete water wave optimization algorithm for no-wait flow shop scheduling problem. Expert Syst Appl 91:347–363
Zhang J, Zhou Y, Luo Q (2018) An improved sine cosine water wave optimization algorithm for global optimization. J Intell Fuzzy Syst 34(4):2129–2141
Shao Z, Pi D, Shao W (2019) A novel multi-objective discrete water wave optimization for solving multi-objective blocking flow-shop scheduling problem. Knowl-Based Syst 165:110–131
Liu A, Li P, Sun W, Deng X, Li W, Zhao Y, Liu B (2019) Prediction of mechanical properties of micro-alloyed steels via neural networks learned by water wave optimization. Neural Comput Appl:1–16
Zhou Y, Zhang J, Yang X, Ling Y (2018) Optimal reactive power dispatch using water wave optimization algorithm. Oper Res:1–17
Ibrahim AM, Tawhid MA, Ward RK (2020) A binary water wave optimization for feature selection. Int J Approximate Reasoning 120:74–91
Manshahia MS (2017) Water wave optimization algorithm-based congestion control and quality of service improvement in wireless sensor networks. Trans Netw Commun 5(4):31–31
Hematabadi AA, Foroud AA (2019) Optimizing the multi-objective bidding strategy using min–max technique and modified water wave optimization method. Neural Comput Appl 31(9):5207–5225
Soltanian A, Derakhshan F, Soleimanpour-Moghadam M (2018) MWWO: modified water wave optimization. In: 2018 3rd conference on swarm intelligence and evolutionary computation (CSIEC). IEEE, pp 1–5
Singh T (2020) A chaotic sequence-guided Harris hawks optimizer for data clustering. Neural Comput Appl
Tsai CW, Chang WY, Wang YC, Chen H (2019) A high-performance parallel coral reef optimization for data clustering. Soft Comput 23(19):9327–9340
Kuwil FH, Shaar F, Topcu AE, Murtagh F (2019) A new data clustering algorithm based on critical distance methodology. Expert Syst Appl 129:296–310
Baalamurugan KM, Bhanu SV (2019) An efficient clustering scheme for cloud computing problems using metaheuristic algorithms. Cluster Comput 22(5):12917–12927
Sharma M, Chhabra JK (2019) An efficient hybrid PSO polygamous crossover-based clustering algorithm. Evol Intell:1–19
Abdulwahab HA, Noraziah A, Alsewari AA, Salih SQ (2019) An enhanced version of black hole algorithm via levy flight for optimization and data clustering problems. IEEE Access 7:142085–142096
Mustafa HM, Ayob M, Nazri MZA, Kendall G (2019) An improved adaptive memetic differential evolution optimization algorithm for data clustering problems. PLoS ONE 14(5):e0216906
Tarkhaneh O, Moser I (2019) An improved differential evolution algorithm using Archimedean spiral and neighborhood search-based mutation approach for cluster analysis. Fut Gener Comput Syst 101:921–939
Aljarah I, Mafarja M, Heidari AA, Faris H, Mirjalili S (2020) Clustering analysis using a novel locality-informed grey wolf-inspired clustering approach. Knowl Inf Syst 62(2):507–539
Zhu LF, Wang JS, Wang HY, Guo SS, Guo MW, Xie W (2020) Data clustering method based on improved bat algorithm with six convergence factors and local search operators. IEEE Access 8:80536–80560
Senthilnath J, Kulkarni S, Suresh S, Yang XS, Benediktsson JA (2019) FPA clust: evaluation of the flower pollination algorithm for data clustering. Evol Intell:1–11
Mageshkumar C, Karthik S, Arunachalam VP (2019) Hybrid metaheuristic algorithm for improving the efficiency of data clustering. Cluster Comput 22(1):435–442
Kaur A, Pal SK, Singh AP (2019) Hybridization of chaos and flower pollination algorithm over k-means for data clustering. Appl Soft Comput:105523
Xie H, Zhang L, Lim CP, Yu Y, Liu C, Liu H, Walters J (2019) Improving K-means clustering with enhanced Firefly Algorithms. Appl Soft Comput 84:105763
Huang KW, Wu ZX, Peng HW, Tsai MC, Hung YC, Lu YC (2019) Memetic particle gravitation optimization algorithm for solving clustering problems. IEEE Access 7:80950–80968
Dinkar SK, Deep K (2019) Opposition-based antlion optimizer using Cauchy distribution and its application to data clustering problem. Neural Comput Appl:1–29
Abualigah LM, Khader AT, Hanandeh ES, Gandomi AH (2017) A novel hybridization strategy for krill herd algorithm applied to clustering techniques. Appl Soft Comput 60:423–435
Zeng N, Wang Z, Zhang H, Kim KE, Li Y, Liu X (2019) An improved particle filter with a novel hybrid proposal distribution for quantitative analysis of gold immunochromatographic strips. IEEE Trans Nanotechnol 18:819–829
Zeng N, Wang Z, Liu W, Zhang H, Hone K, Liu X (2020) A dynamic neighborhood-based switching particle swarm optimization algorithm. IEEE Trans Cybern
Abualigah L (2020) Group search optimizer: a nature-inspired meta-heuristic optimization algorithm with its results, variants, and applications. Neural Comput Appl:1–24
Abualigah L (2020) Multi-verse optimizer algorithm: a comprehensive survey of its results, variants, and applications. Neural Comput Appl:1–21
Zeng N, Qiu H, Wang Z, Liu W, Zhang H, Li Y (2018) A new switching-delayed-PSO-based optimized SVM algorithm for diagnosis of Alzheimer’s disease. Neurocomputing 320:195–202
Zhu G, Kwong S (2010) Gbest-guided artificial bee colony algorithm for numerical function optimization. Appl Math Comput 217(7):3166–3173
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Kaur, A., Kumar, Y. A new metaheuristic algorithm based on water wave optimization for data clustering. Evol. Intel. 15, 759–783 (2022). https://doi.org/10.1007/s12065-020-00562-x
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12065-020-00562-x