Abstract
Clustering is the process of partition of samples, which have not got any labels, into groups. The main aim of clustering was to achieve the lowest distance between samples in each cluster and to achieve the highest distance between the samples in a cluster with the samples in other clusters. In this paper, a novel clustering approach was proposed. This novel approach was built on the differential evolution, which is a meta-heuristic method that searches for the optimum solution, and the randomized artificial neural network, which is a kind of artificial neural network that has a single hidden layer. To evaluate and validate the proposed approach, 48 datasets were employed. Achieved results by the proposed approach were compared with the obtained results by k-means, hierarchical, k-centers clustering, and some other versions of the proposed approach, which are built on ANN and particle swarm optimization, and harmony search methods. It was found that the proposed approach is successful enough to be employed for clustering in terms of achieved inner and outer distance.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Abraham A, Das S, Konar A (2006) Document clustering using differential evolution. In: IEEE congress on evolutionary computation, pp 1784–1791
Bajpai A, Varshney U, Dubey D (2018) Performance enhancement of automatic speech recognition system using Euclidean distance comparison and artificial neural network. In: 2018 3rd international conference on internet of things: smart innovation and usages (IoT-SIU). IEEE, pp 1–5
Das S, Suganthan PN (2011) Differential evolution: a survey of the state-of-the-art. IEEE Trans Evolut Comput 15(1):4–31
Das S, Abraham A, Konar A (2008) Automatic clustering using an improved differential evolution algorithm. IEEE Trans Syst Man Cybern-Part A: Syst Hum 38(1):218–237
Du KL (2010) Clustering: a neural network approach. Neural Netw 23(1):89–107
Duan L, Yuan B, Cui S, Miao J, Zhu W (2016) KELMC: an improved K-means clustering method using extreme learning machine. In: Proceedings of ELM-2015 volume 2. Springer, Cham, pp 273–283
Duin RPW, Juszczak P, Paclik P, Pekalska E, de Ridder D, Tax DMJ (2004) PRTools4, a Matlab toolbox for pattern recognition. Delft University of Technology, Delft
Frey BJ, Dueck D (2007) Clustering by passing messages between data points. Science 315(5814):972–976
Geem ZW, Kim JH, Loganathan GV (2001) A new heuristic optimization algorithm: harmony search. Simulation 76(2):60–68
He Q, Jin X, Du C, Zhuang F, Shi Z (2014) Clustering in extreme learning machine feature space. Neurocomputing 128:88–95
Huang GB, Zhu QY, Siew CK (2004) Extreme learning machine: a new learning scheme of feedforward neural networks. Neural Netw 2:985–990
Huang GB, Zhu QY, Siew CK (2006) Extreme learning machine: theory and applications. Neurocomputing 70(1–3):489–501
Huang G, Liu T, Yang Y, Lin Z, Song S, Wu C (2015) Discriminative clustering via extreme learning machine. Neural Netw 70:1–8
Huang J, Yu ZL, Gu Z (2018) A clustering method based on extreme learning machine. Neurocomputing 277:108–119
Kumar N, Joshi RS (2007) Data clustering using artificial neural networks. In: Proceedings of national conference on challenges & opportunities in information technology (COIT-2007), pp 197–200
Kwedlo W (2011) A clustering method combining differential evolution with the K-means algorithm. Pattern Recognit Lett 32(12):1613–1621
Lichman M (2013) UCI Machine Learning Repository. Irvine, CA Univ. California, Sch. Inf. Comput. Sci. http://archive.ics.uci.edu/ml. Accessed 10 Jan 2019
Litinskii LB, Romanov DE (2006) Neural network clustering based on distances between objects. In: International conference on artificial neural networks. Springer, Berlin, pp 437–443
Liu G, Guo Z (2016) A clustering-based differential evolution with random-based sampling and Gaussian sampling. Neurocomputing 205:229–246
Pao YH, Park GH, Sobajic DJ (1994) Learning and generalization characteristics of the random vector functional-link net. Neurocomputing 6(2):163–180
Park HS, Jun CH (2009) A simple and fast algorithm for K-medoids clustering. Expert Syst Appl 36(2):3336–3341
Paterlini S, Krink T (2004) High performance clustering with differential evolution. In: Congress on evolutionary computation, 2004. CEC2004, vol 2. IEEE
Peng Y, Lu BL (2017) Discriminative extreme learning machine with supervised sparsity preserving for image classification. Neurocomputing 261:242–252
Peng Y, Zheng WL, Lu BL (2016) An unsupervised discriminative extreme learning machine and its applications to data clustering. Neurocomputing 174:250–264
Peng Y, Kong W, Yang B (2017) Orthogonal extreme learning machine for image classification. Neurocomputing 266:458–464
Price K, Storn RM, Lampinen JA (2006) Differential evolution: a practical approach to global optimization. Springer, Berlin
Reddy CK, Vinzamuri B (2018) A survey of partitional and hierarchical clustering algorithms. In: Data clustering. Chapman and Hall/CRC, pp 87–110
Roohi F (2013) Artificial neural network approach to clustering. Int J Eng Sci (IJES) 2(3):33–38
Schmidt WF, Kraaijveld MA, Duin RP (1992) Feedforward neural networks with random weights. In: 11th IAPR international conference on pattern recognition, 1992. Vol II. Conference B: pattern recognition methodology and systems, proceedings. IEEE, pp 1–4
Storn R, Price K (1995) Differential evolution—a simple and efficient adaptive scheme for global optimization over continuous spaces [R]. Berkeley: International Computer Science Institute (ICSI), USA, Technical Report TR-95-012. http://icsi.berkeley.edu/~storn/litera.html. Accessed 10 Jan 2019
Storn R, Price K (1997) Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces. J Global Optim 11(4):341–359
Wang Q, Dou Y, Liu X, Lv Q, Li S (2016) Multi-view clustering with extreme learning machine. Neurocomputing 214:483–494
Wang D, Tan D, Liu L (2018) Particle swarm optimization algorithm: an overview. Soft Comput 22(2):387–408
Zhang L, Suganthan PN (2016) A survey of randomized algorithms for training neural networks. Inf Sci 364:146–155
Zhu QY, Qin AK, Suganthan PN, Huang GB (2005) Evolutionary extreme learning machine. Pattern Recognit 38(10):1759–1763
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
Author Ömer Faruk Ertuğrul declares that he has no conflict of interest.
Ethical approval
This article does not contain any studies with human participants or animals performed by any of the authors.
Additional information
Communicated by V. Loia.
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
Ertuğrul, Ö.F. A novel clustering method built on random weight artificial neural networks and differential evolution. Soft Comput 24, 12067–12078 (2020). https://doi.org/10.1007/s00500-019-04647-3
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00500-019-04647-3