Automatic Clustering Using Multi-objective Particle Swarm and Simulated Annealing

Abubaker, Ahmad A.; Baharum, Adam; Alrefaei, Mahmoud H.

doi:10.1371/journal.pone.0130995

Cited by 42 publications

(16 citation statements)

References 26 publications

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“…The calculation of entropy is shown in Equation ( 13), which measures the entropy for single clustering ; [41]. The total entropy of the clustering is calculated in Equation (14).…”

Section: Resultsmentioning

confidence: 99%

“…Other studies have employed hybrid algorithms, merging the advantages of multi-objective PSO and simulated annealing for automated clustering. In particular, three validity indices are simultaneously optimized as a single-objective function to produce a suitable number of clusters [14]. Related research uses a hybrid algorithm that combines differential equations and fuzzy c-means for clustering.…”

Section: Introductionmentioning

confidence: 99%

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Improved Self-Adaptive ACS Algorithm to Determine the Optimal Number of Clusters

Jabbar

Ku-Mahamud

Sagban

2021

Int. J. Adv. Sci. Eng. Inf. Technol.

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A fundamental problem in data clustering is how to determine the correct number of clusters. The k-adaptive medoid set ant colony optimization (ACO) clustering (METACOC-K) algorithm is superior in solving clustering problems. However, METACOC-K does not guarantee in finding the best number of clusters. It assumed the number of clusters based on an adaptive parameter strategy that lacks feedback learning. This has restrained the algorithm in producing compact clusters and the optimal number of clusters. In this paper, a self-adaptive ACO clustering (S-ACOC) algorithm is proposed to produce the optimal number of clusters by incorporating a self-adaptive parameter strategy. The S-ACOC algorithm is a centroid-based algorithm that automatically adjusts the number of clusters during the algorithm run. The selection of the number of clusters is based on a construction graph that reflects the influence of a pheromone in algorithm learning. Experiments were conducted on real-world datasets to evaluate the performance of the proposed algorithm. The external evaluation metrics (purity, F-measure, and entropy) were used to compare the results of the proposed algorithm with other swarm clustering algorithms, including a genetic algorithm (GA), particle swarm optimization (PSO), and METACOC-K. Results showed that S-ACOC provides higher purity (50%) and lower entropy (40%) than GA, PSO, and METACOC-K. Experiments were also performed on several predefined clusters, and results demonstrate that the S-ACOC algorithm is superior to GA, PSO, and METACOC-K. Based on the superior performance, S-ACOC can be used to solve clustering problems in various application domains.

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“…The calculation of entropy is shown in Equation ( 13), which measures the entropy for single clustering ; [41]. The total entropy of the clustering is calculated in Equation (14).…”

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Improved Self-Adaptive ACS Algorithm to Determine the Optimal Number of Clusters

Jabbar

Ku-Mahamud

Sagban

2021

Int. J. Adv. Sci. Eng. Inf. Technol.

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show abstract

“…In order to obtain representative partitions of the dataset, the LibraryMCS v0.7 (ChemAxon, 2011) hierarchical clustering procedure has been jointly applied with the k-means optimization clustering algorithm implemented in Statistica 10 Cluster Analysis Module (Statsoft Inc., 2011). In the recent years, many novel clustering algorithms have been developed for different applications [26][27][28][29][30]. The LibraryMCS is a hierarchical clustering procedure that uses the maximum common substructure (MCS, the largest subgraph found in two chemical graphs) in combination with molecular fingerprints to group a set of small molecules.…”

Section: Dataset Compilation and Partition Into Representative Training And Test Setsmentioning

confidence: 99%

Integrated Application of Enhanced Replacement Method and Ensemble Learning for the Prediction of BCRP/ABCG2 Substrates

Gantner

Alberca

Mercader

et al. 2017

CBIO

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Breast Cancer Resistance Protein (BCRP or ABCG2) is a polyspecific efflux-transporter which belongs to the ATP-binding Cassette superfamily. Up-regulation of BCRP is associated to multi-drug resistance in a number of conditions, e.g. cancer and epilepsy. Recent proteomic studies show that high-expression levels of BCRP are found in healthy human intestine and at the blood-brain barrier, limiting the absorption and brain distribution of its substrates. Here, we have jointly applied the Enhanced Replacement Method and ensemble learning approaches to obtain combinations of 2D linear classifiers capable of discriminating among substrates and non-substrates of the wild type human BCRP. The best model ensemble obtained outperforms previously reported 2D linear classifiers, showing the ability of the Enhanced Replacement Method and ensemble learning schemes to optimize the performance of individual models. This is the first report of the Enhanced Replacement Method to solve classification problems.

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“…Before continuing with the description of some multi-objective clustering methods, we should note that the majority of those methods deals with the compactness, the total symmetry and the connectedness of clusters. The compactness of the clustering measures how close are the objects that belong to the same cluster, the total symmetry of the clusters evaluates how the clusters candidates are symmetry distributed regarding the center while the connectedness of the clusters measures how separated clusters are connected Multi-objective Particle Swarm Optimization and Simulated Annealing algorithm, MOPSOSA was used in [1]. To product a goods clustering solutions, this algorithm optimizes simultaneously three cluster validity indices: the DB-Index which takes into account the compactness of the clustering and is established using the Euclidean distance, the Sym-Index which is looking for the total symmetry and is centered on the point symmetry distance and finally the Con-Index which examines the connectedness and is established using the short distance.…”

Section: Introductionmentioning

confidence: 99%

Multi-objective PSO with Pareto Neighborhood topology for Clustering

Soussi¹,

Rokbani²,

Wali³

et al. 2021

Preprint

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In this paper a new technique is integrated to Multi-Objective Particle Swarm Optimization (MOPSO) algorithm, named Pareto Neighborhood (PN) topology, to produce MOPSO-PN algorithm. This technique involves iteratively selecting a set of best solutions from the Pareto-Optimal-Fronts and trying to explore them in order to find better clustering results in the next iteration. MOPSO-PN was then used as a Multi?Objective Clustering Optimization (MOCO) Algorithm, it was tested on various datasets (real-life and artificial datasets). Two scenarios have been used to test the performances of MOPSO-PN for clustering: In the first scenario MOPSO-PN utilizes, as objective functions, two clusters validity index (Silhouette?Index and overall-cluster-deviation), three datasets for test, four algorithms for comparison and the average Minkowski Score as metric for evaluating the final clustering result; In the second scenario MOPSO-PN used, as objectives functions, three clusters validity index (I-index, Con-index and Sym?index), 20 datasets for test, ten algorithms for comparison and the F-Measure as metric for evaluating the final clustering result. In both scenarios, MOPSO-PN provided a competitive clustering results and a correct number of clusters for all datasets.

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Automatic Clustering Using Multi-objective Particle Swarm and Simulated Annealing

Cited by 42 publications

References 26 publications

Improved Self-Adaptive ACS Algorithm to Determine the Optimal Number of Clusters

Improved Self-Adaptive ACS Algorithm to Determine the Optimal Number of Clusters

Integrated Application of Enhanced Replacement Method and Ensemble Learning for the Prediction of BCRP/ABCG2 Substrates

Multi-objective PSO with Pareto Neighborhood topology for Clustering

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