research-article
Authors: Cristina Yassue Morimoto, Aurora Trinidad Ramirez Pozo, and Marcílio Carlos Pereira de Souto
Volume 245, Issue C
Published: 02 July 2024 Publication History
- 0citation
- 0
- Downloads
Metrics
Total Citations0Total Downloads0Last 12 Months0
Last 6 weeks0
New Citation Alert added!
This alert has been successfully added and will be sent to:
You will be notified whenever a record that you have chosen has been cited.
To manage your alert preferences, click on the button below.
Manage my Alerts
New Citation Alert!
Please log in to your account
- View Options
- References
- Media
- Tables
- Share
Abstract
Evolutionary multi-objective clustering (EMOC) is a modern clustering technique in which the general concepts of evolutionary multi-objective optimization are applied to the clustering problem. The design and definition of clustering are difficult problems, and the choice of the objective functions and parameter setting of the algorithms are still challenges. In this paper, we propose the adaptive evolutionary multi-objective clustering approach based on data properties (AEMOC). AEMOC considers a new metric to evaluate the base partitions (candidate solutions used in the initial population) generated by minimum spanning tree clustering. This metric is applied to: (1) determine properties of the data, such as separation and overlap; and (2) define an offline selection of objective functions and parameter settings of the multi-objective algorithm. The information regarding the data properties allows AEMOC to avoid unnecessary data processing in datasets in which the initialization provides optimal solutions and optimization is not required. AEMOC presented promising results considering a diverse set of artificial and real-life datasets, based on two aspects: (1) it succeeded in the determination of the data properties of the base partitions and verifying the potential clustering quality, presenting a correlation of 0.8 with a reference metric, and (2) it provided better clustering results than reference EMOC approaches, achieving a general gain in the clustering performance of 3% in real-life datasets and 7% in artificial datasets.
References
[1]
Adam A., Blockeel H., Constraint-based measure for estimating overlap in clustering, in: Benelearn 2017: Proceedings of the twenty-sixth benelux conference on machine learning, 2017, pp. 54–61.
[2]
Corne D.W., Jerram N.R., Knowles J.D., Oates M.J., PESA-II: Region-based selection in evolutionary multiobjective optimization, in: Proceedings of the 3rd annual conference on genetic and evolutionary computation, Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, 2001, pp. 283–290.
[3]
Deb K., Pratap A., Agarwal S., Meyarivan T., A fast and elitist multiobjective genetic algorithm: NSGA-II, IEEE Transactions on Evolutionary Computation 6 (2) (2002) 182–197.
Digital Library
[4]
Ertöz L., Steinbach M., Kumar V., A new shared nearest neighbor clustering algorithm and its applications, in: Workshop on clustering high dimensional data and its applications at 2nd SIAM international conference on data mining, 2002.
[5]
Faceli K., de Carvalho A.C.P.L.F., de Souto M.C.P., Multi-objective clustering ensemble, in: 2006 Sixth international conference on hybrid intelligent systems (HIS0́6), IEEE, Rio de Janeiro, Brazil, 2006, p. 51,.
[6]
Fern X.Z., Brodley C.E., Solving cluster ensemble problems by bipartite graph partitioning, in: Proceedings of the twenty-first international conference on machine learning, in: ICML 0́4, ACM, New York, NY, USA, 2004, p. 36,.
Digital Library
[7]
Friedman M., A comparison of alternative tests of significance for the problem of m rankings, The Annals of Mathematical Statistics 11 (1) (1940) 86–92.
[8]
Garza-Fabre M., Handl J., Knowles J., An improved and more scalable evolutionary approach to multiobjective clustering, IEEE Transactions on Evolutionary Computation 22 (4) (2017) 515–535,.
[9]
Hancer E., Karaboga D., A comprehensive survey of traditional, merge-split and evolutionary approaches proposed for determination of cluster number, Swarm and Evolutionary Computation 32 (2017) 49–67,.
[10]
Handl J., Knowles J., Improvements to the scalability of multiobjective clustering, in: 2005 IEEE congress on evolutionary computation, vol. 3, IEEE, Edinburgh, UK, 2005, pp. 2372–2379,.
[11]
Handl J., Knowles J., An evolutionary approach to multiobjective clustering, IEEE Transactions on Evolutionary Computation 11 (1) (2007) 56–76,.
Digital Library
[12]
Hruschka E.R., Campello R.J.G.B., Freitas A.A., de Carvalho A.C.P.L.F., A survey of evolutionary algorithms for clustering, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews) 39 (2) (2009) 133–155,.
Digital Library
[13]
Hubert L., Arabie P., Comparing partitions, Journal of Classification 2 (1) (1985) 193–218.
[14]
Jain A., Data clustering: 50 years beyond K-means, Pattern Recognition Letters 31 (2010) 651–666,.
Digital Library
[15]
Jain A.K., Murty M.N., Flynn P.J., Data clustering: a review, ACM Computing Surveys (CSUR) 31 (3) (1999) 264–323.
Digital Library
[16]
Jaskowiak P.A., Costa I.G., Campello R.J.G.B., The area under the ROC curve as a measure of clustering quality, Data Mining and Knowledge Discovery 36 (3) (2022) 1219–1245,.
Digital Library
[17]
Li J., Liu R., Zhang M., Li Y., Ensemble-based multi-objective clustering algorithms for gene expression data sets, in: 2017 IEEE congress on evolutionary computation (CEC), IEEE, Donostia, Spain, 2017, pp. 333–340,.
Digital Library
[18]
Liu R., Liu Y., Li Y., An improved method for multi-objective clustering ensemble algorithm, in: 2012 IEEE congress on evolutionary computation, IEEE, Brisbane, QLD, Australiac, 2012, pp. 1–8,.
[19]
Liu C., Liu J., Peng D., Wu C., A general multiobjective clustering approach based on multiple distance measures, IEEE Access 6 (2018) 41706–41719,.
[20]
MacQueen J., Some methods for classification and analysis of multivariate observations, in: Proceedings of the fifth berkeley symposium on mathematical statistics and probability, volume 1: Statistics, University of California Press, Berkeley, Calif, 1967, pp. 281–297.
[21]
Matake N., Hiroyasu T., Miki M., Senda T., Multiobjective clustering with automatic K-determination for large-scale data, in: Proceedings of the 9th annual conference on genetic and evolutionary computation, GECCO ’07, Association for Computing Machinery, New York, NY, USA, 2007, pp. 861–868,.
Digital Library
[22]
Morimoto C.Y., Pozo A., de Souto M.C., An analysis of the admissibility of the objective functions applied in evolutionary multi-objective clustering, Information Sciences 610 (2022) 1143–1162,.
Digital Library
[23]
Morimoto C.Y., Pozo A.T.R., de Souto M.C.P., A review of evolutionary multi-objective clustering approaches, 2021, arXiv, Preprint submitted to Computer Science Review, arXiv:2110.08100.
[24]
Morimoto C.Y., Pozo A., de Souto M.C.P., Detecting nested structures through evolutionary multi-objective clustering, in: Applications of evolutionary computation: 25th European conference, evoapplications 2022, held as part of evostar 2022, Madrid, Spain, April 20–22, 2022, proceedings, Springer-Verlag, Berlin, Heidelberg, 2022, pp. 369–385,.
Digital Library
[25]
Pohlert T., PMCMR: Calculate pairwise multiple comparisons of mean rank sums, 2018, URL: https://CRAN.R-project.org/package=PMCMR.
[26]
Russell S., Norvig P., Artificial intelligence: A modern approach, 2002.
Digital Library
[27]
Siarry P., Metaheuristics, Springer, Cham,Switzerland, 2016,.
[28]
Sneath P.H., The application of computers to taxonomy, Microbiology 17 (1) (1957) 201–226.
[29]
Sokal R.R., A statistical method for evaluating systematic relationships, University of Kansas Science Bulletin 38 (1958) 1409–1438.
[30]
Strehl A., Relationship-based clustering and cluster ensembles for high-dimensional data mining, (Ph.D. thesis) The University of Texas, 2002, AAI3088578.
[31]
Tsai C., Chen W., Chiang M., A modified multiobjective EA-based clustering algorithm with automatic determination of the number of clusters, in: 2012 IEEE international conference on systems, man, and cybernetics (SMC), IEEE, Seoul, Korea (South), 2012, pp. 2833–2838,.
[32]
Wang L., Cui G., Zhou Q., Li K., A multi-clustering method based on evolutionary multiobjective optimization with grid decomposition, Swarm and Evolutionary Computation 55 (2020),.
[33]
Wang R., Lai S., Wu G., Xing L., Wang L., Ishibuchi H., Multi-clustering via evolutionary multi-objective optimization, Information Sciences 450 (2018) 128–140,.
Digital Library
[34]
Xu R., Wunsch D., Survey of clustering algorithms, IEEE Transactions on Neural Networks 16 (3) (2005) 645–678.
Digital Library
[35]
Zahn C., Graph-theoretical methods for detecting and describing gestalt clusters, IEEE Transactions on Computers C-20 (1) (1971) 68–86,.
Digital Library
[36]
Zhu S., Xu L., Cao L., A study of automatic clustering based on evolutionary many-objective optimization, in: Proceedings of the genetic and evolutionary computation conference companion, GECCO ’18, Association for Computing Machinery, New York, NY, USA, 2018, pp. 173–174,.
Digital Library
[37]
Zhu S., Xu L., Goodman E.D., Evolutionary multi-objective automatic clustering enhanced with quality metrics and ensemble strategy, Knowledge-Based Systems 188 (2020).
[38]
Zhu S., Xu L., Goodman E.D., Hierarchical topology-based cluster representation for scalable evolutionary multiobjective clustering, IEEE Transactions on Cybernetics (2021) 1–15,.
[39]
Zitzler, E., Laumanns, M., & Thiele, L. (2001). SPEA2: Improving the strength Pareto evolutionary algorithm: TIK-report, 103.
[40]
Zitzler E., Thiele L., Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach, IEEE Transactions on Evolutionary Computation 3 (4) (1999) 257–271,.
Digital Library
Recommendations
- An analysis of the admissibility of the objective functions applied in evolutionary multi-objective clustering
Abstract
A variety of clustering criteria have been applied as an objective function in Evolutionary Multi-Objective Clustering approaches (EMOCs). However, most EMOCs do not provide detailed analysis regarding the choice and usage of the ...
Read More
- Detecting Nested Structures Through Evolutionary Multi-objective Clustering
Applications of Evolutionary Computation
Abstract
The evolutionary multi-objective algorithms have been widely applied for clustering. However, in general, the detection of heterogeneous nested clusters remains challenging for clustering algorithms. This paper proposes an adaptation of the ...
Read More
- Automatic multi-objective clustering based on game theory
Novel multi-objective clustering algorithm based-sequential games was proposed.It optimizes simultaneously and efficiently multiple conflicting objectives.Proposed approach can automatically calculate the optimal number of clusters.This algorithm shows ...
Read More
Comments
Information & Contributors
Information
Published In
Expert Systems with Applications: An International Journal Volume 245, Issue C
Jul 2024
1580 pages
ISSN:0957-4174
Issue’s Table of Contents
Elsevier Ltd.
Publisher
Pergamon Press, Inc.
United States
Publication History
Published: 02 July 2024
Author Tags
- Clustering criteria
- Multi-objective clustering
- Evolutionary multi-objective optimization
- Clustering analysis
Qualifiers
- Research-article
Contributors
Other Metrics
View Article Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
Total Citations
Total Downloads
- Downloads (Last 12 months)0
- Downloads (Last 6 weeks)0
Other Metrics
View Author Metrics
Citations
View Options
View options
Get Access
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in
Full Access
Get this Publication
Media
Figures
Other
Tables
