Multi-population Black Hole Algorithm for the problem of data clustering

The retrieval of important information from a dataset requires applying a special data mining technique known as data clustering (DC). DC classifies similar objects into a groups of similar characteristics. Clustering involves grouping the data around k-cluster centres that typically are selected ra...

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Vydáno v:PloS one Ročník 18; číslo 7; s. e0288044
Hlavní autoři: Salih, Sinan Q., Alsewari, AbdulRahman A., Wahab, H. A., Mohammed, Mustafa K. A., Rashid, Tarik A., Das, Debashish, Basurra, Shadi S.
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States Public Library of Science 05.07.2023
Public Library of Science (PLoS)
Témata:
Algorithms
Analysis
Benchmarks
Black holes
Cluster analysis
Clustering
Computer and Information Sciences
Data analysis
Data mining
Datasets
Engineering
Evaluation
Genetic algorithms
Heuristic
Heuristic methods
Information retrieval
Machine learning
Mathematical optimization
Mathematical programming
Optimization
Optimization algorithms
Physical Sciences
Research and Analysis Methods
Social Sciences
Solution space
Iraq
ISSN:1932-6203, 1932-6203
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Popis
Shrnutí:The retrieval of important information from a dataset requires applying a special data mining technique known as data clustering (DC). DC classifies similar objects into a groups of similar characteristics. Clustering involves grouping the data around k-cluster centres that typically are selected randomly. Recently, the issues behind DC have called for a search for an alternative solution. Recently, a nature-based optimization algorithm named Black Hole Algorithm (BHA) was developed to address the several well-known optimization problems. The BHA is a metaheuristic (population-based) that mimics the event around the natural phenomena of black holes, whereby an individual star represents the potential solutions revolving around the solution space. The original BHA algorithm showed better performance compared to other algorithms when applied to a benchmark dataset, despite its poor exploration capability. Hence, this paper presents a multi-population version of BHA as a generalization of the BHA called MBHA wherein the performance of the algorithm is not dependent on the best-found solution but a set of generated best solutions. The method formulated was subjected to testing using a set of nine widespread and popular benchmark test functions. The ensuing experimental outcomes indicated the highly precise results generated by the method compared to BHA and comparable algorithms in the study, as well as excellent robustness. Furthermore, the proposed MBHA achieved a high rate of convergence on six real datasets (collected from the UCL machine learning lab), making it suitable for DC problems. Lastly, the evaluations conclusively indicated the appropriateness of the proposed algorithm to resolve DC issues.
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Competing Interests: he authors have declared that no competing interests exist.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0288044