Skip to main content
SpringerLink
Log in

Combination of Cooperative Grouper Fish -- Octopus Algorithm and DBSCAN to Automatic Clustering

  • Living reference work entry
  • First Online:
Handbook of Formal Optimization

  • 20 Accesses

Abstract

Density-based spatial clustering of applications with noise (DBSCAN) has been used to cluster data with arbitrary shapes which clustering is done based on the density among objects in data. Given that DBSCAN is a proper tool for identifying outliers and clustering non-convex data, it can be used for automatic clustering of non-convex data and covered the weakness of most automatic clustering algorithms in not recognizing non-convex clusters. So, in this chapter, a new automatic clustering algorithm is introduced which is a combination of DBSCAN and a new metaheuristic algorithm called grouper fish – octopus (GFO) algorithm. GFO-DBSCAN finds the best number of clusters in two main steps in an iterative manner. In the first step, the values of esp and minpts are generated by GFO algorithm, and in the second step, the clustering of data is performed using DBSCAN algorithm with eps and minpts that are generated in the previous step. After each clustering, using correct data labels, and cluster centroids, the Calinski-Harabasz (CH) index is calculated. Finally, after passing some iterations of GFO algorithm, the best number of clusters is reported. In this study, three categories of data are used to measure the performance of the GFO-DBSCAN algorithm. Also, DBSCAN is compared with ACDE, DCPSO, and GCUK algorithms. According to the results, GFO-DBSCAN has achieved the optimal number of clusters in most data and has outperformed other well-known algorithms.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Institutional subscriptions

References

  • Alba E, Dorronsoro B (2005) The exploration/exploitation tradeoff in dynamic cellular genetic algorithms. IEEE Trans Evol Comput 9(2):126–142

    Article  Google Scholar 

  • Armano G, Farmani MR (2016) Multiobjective clustering analysis using particle swarm optimization. Expert Syst Appl 55:184–193

    Article  Google Scholar 

  • Arya R, Sikka G (2014). An optimized approach for density based spatial clustering application with noise. In: ICT and critical infrastructure: proceedings of the 48th annual convention of computer society of India-vol I, Springer

    Google Scholar 

  • Balavand A (2022a) Crocodile hunting strategy (CHS): a comparative study using benchmark functions. Iran J Numer Anal Optim 12(2):397–425

    Google Scholar 

  • Balavand A (2022b) A new feature clustering method based on crocodiles hunting strategy optimization algorithm for classification of MRI images. Vis Comput 38(1):149–178

    Article  Google Scholar 

  • Balavand A et al (2018) Automatic clustering based on crow search algorithm-Kmeans (CSA-Kmeans) and data envelopment analysis (DEA). Int J Comput Intell Syst 11(1):1322–1337

    Article  Google Scholar 

  • Bandyopadhyay S, Maulik U (2002) Genetic clustering for automatic evolution of clusters and application to image classification. Pattern Recogn 35(6):1197–1208

    Article  MATH  Google Scholar 

  • Bandyopadhyay S, Saha S (2008) A point symmetry-based clustering technique for automatic evolution of clusters. IEEE Trans Knowl Data Eng 20(11):1441–1457

    Article  Google Scholar 

  • Bezdek JC (2013) Pattern recognition with fuzzy objective function algorithms. Springer Science & Business Media, Heidelberg

    MATH  Google Scholar 

  • Chang H, Yeung D-Y (2008) Robust path-based spectral clustering. Pattern Recogn 41(1):191–203

    Article  MATH  Google Scholar 

  • Chowdhury A et al (2011) Automatic clustering based on invasive weed optimization algorithm. In: International conference on swarm, evolutionary, and memetic computing, Springer

    Google Scholar 

  • Darong H, Peng W (2012) Grid-based DBSCAN algorithm with referential parameters. Phys Procedia 24:1166–1170

    Article  Google Scholar 

  • Das S et al (2006) Spatial information based image segmentation using a modified particle swarm optimization algorithm. In: Sixth international conference on intelligent systems design and applications, IEEE

    Google Scholar 

  • Das S et al (2008a) Automatic clustering using an improved differential evolution algorithm. IEEE Trans Syst Man Cybern A Syst Hum 38(1):218–237

    Article  Google Scholar 

  • Das S et al (2008b) Automatic kernel clustering with a multi-elitist particle swarm optimization algorithm. Pattern Recogn Lett 29(5):688–699

    Article  Google Scholar 

  • Davies DL, Bouldin DW (1979) A cluster separation measure. IEEE Trans Pattern Anal Mach Intell 1(2):224–227

    Article  Google Scholar 

  • Dharni C, Bnasal M (2013) An improvement of DBSCAN algorithm to analyze cluster for large datasets. In: 2013 IEEE international conference in MOOC, innovation and technology in education (MITE), IEEE

    Google Scholar 

  • Dorigo M et al (1996) Ant system: optimization by a colony of cooperating agents. IEEE Trans Syst Man Cybern B Cybern 26(1):29–41

    Article  Google Scholar 

  • Dorigo M, Thomas S (2019) Ant colony optimization: overview and recent advances. Springer International Publishing

    Google Scholar 

  • Eberhart R, Kennedy J (1995) A new optimizer using particle swarm theory. In: Micro machine and human science, 1995. MHS’95, proceedings of the sixth international symposium on, IEEE

    Google Scholar 

  • Ester M et al (1996) A density-based algorithm for discovering clusters in large spatial databases with noise. In: Proceedings of 2nd international conference on knowledge discovery and data mining (KDD), AAAI Press

    Google Scholar 

  • Fielding A (2007) Cluster and classification techniques for the biosciences. Cambridge University Press, Cambridge

    Google Scholar 

  • Fu L, Medico E (2007) FLAME, a novel fuzzy clustering method for the analysis of DNA microarray data. BMC Bioinformat 8(1):1–15

    Article  Google Scholar 

  • Gionis A et al (2007) Clustering aggregation. ACM Trans Knowl Discov Data 1(1):4

    Article  Google Scholar 

  • Handl J et al (2006) Ant-based clustering and topographic mapping. Artif Life 12(1):35–62

    Article  Google Scholar 

  • Jain AK, Law MH (2005) Data clustering: a user’s dilemma. In: International conference on pattern recognition and machine intelligence, Springer

    Google Scholar 

  • Jiang H et al (2011) A new hybrid method based on partitioning-based DBSCAN and ant clustering. Expert Syst Appl 38(8):9373–9381

    Article  Google Scholar 

  • José-García A, Gómez-Flores W (2016) Automatic clustering using nature-inspired metaheuristics: a survey. Appl Soft Comput 41:192–213

    Article  Google Scholar 

  • Kanade PM, Hall LO (2003) Fuzzy ants as a clustering concept. In: 22nd international conference of the North American Fuzzy Information Processing Society, NAFIPS 2003, IEEE

    Google Scholar 

  • Karami A, Johansson R (2014) Choosing DBSCAN parameters automatically using differential evolution. Int J Comput Appl 91(7):1–11

    Google Scholar 

  • Khan MMR et al (2018) ADBSCAN: adaptive density-based spatial clustering of applications with noise for identifying clusters with varying densities. In: 2018 4th international conference on Electrical Engineering and Information & Communication Technology (iCEEiCT), IEEE

    Google Scholar 

  • Kim M, Ramakrishna R (2005) New indices for cluster validity assessment. Pattern Recogn Lett 26(15):2353–2363

    Article  Google Scholar 

  • Krause EF (2012) Taxicab geometry: an adventure in non-Euclidean geometry. Courier Corporation, Addison-Wesley publishing Company, Menlo Park, California

    Google Scholar 

  • Kuo R-J et al (2014) Automatic kernel clustering with bee colony optimization algorithm. Inf Sci 283:107–122

    Article  Google Scholar 

  • Kuo R-J., Ferani EZ (2019) An improved differential evolution with cluster decomposition algorithm for automatic clustering. Soft Comput 23(18):8957–8973

    Google Scholar 

  • Lin L, Gen M (2009) Auto-tuning strategy for evolutionary algorithms: balancing between exploration and exploitation. Soft Comput A Fusion Found Methodol Appl 13(2):157–168

    MATH  Google Scholar 

  • Lloyd S (1982) Least squares quantization in PCM. IEEE Trans Inf Theory 28(2):129–137

    Article  MathSciNet  MATH  Google Scholar 

  • Maulik U, Bandyopadhyay S (2003) Fuzzy partitioning using a real-coded variable-length genetic algorithm for pixel classification. IEEE Trans Geosci Remote Sens 41(5):1075–1081

    Article  Google Scholar 

  • Mehrabian AR, Lucas C (2006) A novel numerical optimization algorithm inspired from weed colonization. Eco Inform 1(4):355–366

    Article  Google Scholar 

  • Olorunda O, Engelbrecht AP (2008) Measuring exploration/exploitation in particle swarms using swarm diversity. In: Evolutionary computation, 2008. CEC 2008 (IEEE World Congress on Computational Intelligence). IEEE Congress on, IEEE

    Google Scholar 

  • Omran MG et al (2006) Dynamic clustering using particle swarm optimization with application in image segmentation. Pattern Anal Applic 8(4):332–344

    Article  MathSciNet  Google Scholar 

  • Qu J et al (2010) Mixed PSO clustering algorithm using point symmetry distance. J Comput Informat Syst 6(6):2027–2035

    Google Scholar 

  • Rad MH, Abdolrazzagh-Nezhad M (2020) A new hybridization of DBSCAN and fuzzy earthworm optimization algorithm for data cube clustering. Soft Comput 24(20):15529–15549

    Article  Google Scholar 

  • Sheng W et al (2005) A weighted sum validity function for clustering with a hybrid niching genetic algorithm. IEEE Trans Syst Man Cybern B Cybern 35(6):1156–1167

    Article  Google Scholar 

  • Smiti A, Elouedi Z (2012) DBSCAN-GM: an improved clustering method based on Gaussian means and DBSCAN techniques. In: 2012 IEEE 16th international conference on intelligent engineering systems (INES), IEEE

    Google Scholar 

  • Socha K, Dorigo M (2008) Ant colony optimization for continuous domains. Eur J Oper Res 185(3):1155–1173

    Article  MathSciNet  MATH  Google Scholar 

  • Storn R, Price K (1997) Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces. J Glob Optim 11(4):341–359

    Article  MathSciNet  MATH  Google Scholar 

  • Unsworth RK, Cullen-Unsworth LC (2012) An inter-specific behavioural association between a highfin grouper (Epinephelus maculatus) and a reef octopus (Octopus cyanea). Mar Biodivers Rec 5:e97

    Article  Google Scholar 

  • Van Der Heijden F et al (2005) Classification, parameter estimation and state estimation: an engineering approach using MATLAB. Wiley, Hoboken

    Google Scholar 

  • Veenman CJ et al (2002) A maximum variance cluster algorithm. IEEE Trans Pattern Anal Mach Intell 24(9):1273–1280

    Article  Google Scholar 

  • Xu R, Wunsch D (2008) Clustering. Wiley, Piscataway

    Book  Google Scholar 

  • Zhang C et al (2010) An artificial bee colony approach for clustering. Expert Syst Appl 37(7):4761–4767

    Article  Google Scholar 

  • Zhu Q et al (2021) Application of the novel harmony search optimization algorithm for DBSCAN clustering. Expert Syst Appl 178:115054

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Industrial Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran

    Alireza Balavand

Authors
  1. Alireza Balavand
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Alireza Balavand .

Editor information

Editors and Affiliations

  1. Institute of Artificial Intelligence, MIT World Peace University, Pune, Pune, Maharashtra, India

    Anand J. Kulkarni

  2. Faculty of Engineering and IT, University of Technology Sydney, Ultimo, NSW, Australia

    Amir H. Gandomi

Section Editor information

  1. Institute of Artificial Intelligence, MIT World Peace University, Pune, MH, India

    Anand J Kulkarni

Rights and permissions

Reprints and permissions

Copyright information

© 2023 Springer Nature Singapore Pte Ltd.

About this entry

Check for updates. Verify currency and authenticity via CrossMark

Cite this entry

Balavand, A. (2023). Combination of Cooperative Grouper Fish -- Octopus Algorithm and DBSCAN to Automatic Clustering. In: Kulkarni, A.J., Gandomi, A.H. (eds) Handbook of Formal Optimization. Springer, Singapore. https://doi.org/10.1007/978-981-19-8851-6_4-1

Download citation

  • DOI: https://doi.org/10.1007/978-981-19-8851-6_4-1

  • Received:

  • Accepted:

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-19-8851-6

  • Online ISBN: 978-981-19-8851-6

  • eBook Packages: Springer Reference Intelligent Technologies and RoboticsReference Module Computer Science and Engineering

Publish with us

Policies and ethics

Search

Navigation