Semi-supervised data clustering using particle swarm optimisation

Lai, Daphne Teck Ching; Miyakawa, Minami; Sato, Yuji

doi:10.1007/s00500-019-04114-z

Cited by 11 publications

(6 citation statements)

References 11 publications

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“…Recently, the gravitational search algorithm (GSA) proposed by Rashedi et al (2009) has been applied to tackle various optimisation issues such as unconstrained global optimisation problems (García-Ródenas et al, 2019), hydrology (Karami et al, 2019) and in the geothermal power plant optimisation (Özkaraca and Keçebaş, 2019). Particle Swarm Optimisation (PSO) algorithm has been used in different fields such as sediment yield forecasting (Meshram et al, 2019), operation rule derivation of hydropower reservoir (Feng et al, 2019) and semi-supervised data clustering (Lai et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

A Method for Predicting Long-Term Municipal Water Demands Under Climate Change

Zubaidi

Ortega‐Martorell

Kot

et al. 2020

Water Resour Manage

117

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Section: Introductionmentioning

confidence: 99%

A Method for Predicting Long-Term Municipal Water Demands Under Climate Change

Zubaidi

Ortega‐Martorell

Kot

et al. 2020

Water Resour Manage

117

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“…Previously, we were not able to obtain results on CMC, segment and vehicle datasets. Through this analysis here, we are re-evaluating by comparison with the said algorithms and the additional datasets, which usually give poor results, as was reported in [30]. We also compare CM-BBPSO results with the existing cluster-based MOO technique HT-MOC [20], MOC techniques MOFC-TMS [25], VAMOSA [31], GenClustMOO [32], MOAC [26], ES-NMPBFO [27] and BBPSO clustering technique CBPSO [24] based on commonly reported datasets and evaluation metrics.…”

Section: Methodsmentioning

confidence: 97%

“…The pBests from BBPSO are selected based on the quantization error-based fitness function from [23]. BBPSO is used because it requires lesser parameters than PSO and performs well in clustering, based on previous work [30]. Next, we subject the pbest solutions found by BBPSO to the clusterbased MOEA/D algorithm for solution evolving and selection to find the gbest for the BBPSO framework.…”

Section: Cluster-based Moea/d Bbpsomentioning

confidence: 99%

“…The algorithms are tested on 13 datasets obtained from the UCI Machine Learning Repository: Appendicitis, CMC, Dermatology, Ecoli, Glass, Iris, Seeds, Sonar, Vehicle, WDBC, Wine, WOBC and Segment [15]. Full details and t-SNE biplots of the datasets are found in [30]. These datasets have different types of characteristics, such as sizes N, dimensions n and cluster numbers c, as well as having different intensity of densities and overlapping.…”

Section: Datamentioning

confidence: 99%

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An Empirical Study of Cluster-Based MOEA/D Bare Bones PSO for Data Clustering †

Lai

Sato

2021

Algorithms

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Previously, cluster-based multi or many objective function techniques were proposed to reduce the Pareto set. Recently, researchers proposed such techniques to find better solutions in the objective space to solve engineering problems. In this work, we applied a cluster-based approach for solution selection in a multiobjective evolutionary algorithm based on decomposition with bare bones particle swarm optimization for data clustering and investigated its clustering performance. In our previous work, we found that MOEA/D with BBPSO performed the best on 10 datasets. Here, we extend this work applying a cluster-based approach tested on 13 UCI datasets. We compared with six multiobjective evolutionary clustering algorithms from the existing literature and ten from our previous work. The proposed technique was found to perform well on datasets highly overlapping clusters, such as CMC and Sonar. So far, we found only one work that used cluster-based MOEA for clustering data, the hierarchical topology multiobjective clustering algorithm. All other cluster-based MOEA found were used to solve other problems that are not data clustering problems. By clustering Pareto solutions and evaluating new candidates against the found cluster representatives, local search is introduced in the solution selection process within the objective space, which can be effective on datasets with highly overlapping clusters. This is an added layer of search control in the objective space. The results are found to be promising, prompting different areas of future research which are discussed, including the study of its effects with an increasing number of clusters as well as with other objective functions.

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“…Among these methods, PSO is famous for fast convergence and easy applying. PSO algorithms are widely applied in engineering like route planning [ 8 , 9 ], data clustering [ 10 , 11 ], feature selection [ 12 , 13 ], image segmentation [ 14 , 15 ], power system [ 16 , 17 ], engineering areas [ 18 – 20 ], and so on.…”

Section: Introductionmentioning

confidence: 99%

An electronic transition-based bare bones particle swarm optimization algorithm for high dimensional optimization problems

et al. 2022

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An electronic transition-based bare bones particle swarm optimization (ETBBPSO) algorithm is proposed in this paper. The ETBBPSO is designed to present high precision results for high dimensional single-objective optimization problems. Particles in the ETBBPSO are divided into different orbits. A transition operator is proposed to enhance the global search ability of ETBBPSO. The transition behavior of particles gives the swarm more chance to escape from local minimums. In addition, an orbit merge operator is proposed in this paper. An orbit with low search ability will be merged by an orbit with high search ability. Extensive experiments with CEC2014 and CEC2020 are evaluated with ETBBPSO. Four famous population-based algorithms are also selected in the control group. Experimental results prove that ETBBPSO can present high precision results for high dimensional single-objective optimization problems.

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Semi-supervised data clustering using particle swarm optimisation

Cited by 11 publications

References 11 publications

A Method for Predicting Long-Term Municipal Water Demands Under Climate Change

A Method for Predicting Long-Term Municipal Water Demands Under Climate Change

An Empirical Study of Cluster-Based MOEA/D Bare Bones PSO for Data Clustering †

An electronic transition-based bare bones particle swarm optimization algorithm for high dimensional optimization problems

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