Performance evaluation results of evolutionary clustering algorithm star for clustering heterogeneous datasets
This article presents the data used to evaluate the performance of evolutionary clustering algorithm star (ECA*) compared to five traditional and modern clustering algorithms. Two experimental methods are employed to examine the performance of ECA* against genetic algorithm for clustering++ (GENCLUS...
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| Format: | Article |
| Language: | English |
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Elsevier
2021-06-01
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| Series: | Data in Brief |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352340921003280 |
| _version_ | 1818462045734961152 |
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| author | Bryar A. Hassan Tarik A. Rashid Seyedali Mirjalili |
| author_facet | Bryar A. Hassan Tarik A. Rashid Seyedali Mirjalili |
| author_sort | Bryar A. Hassan |
| collection | DOAJ |
| description | This article presents the data used to evaluate the performance of evolutionary clustering algorithm star (ECA*) compared to five traditional and modern clustering algorithms. Two experimental methods are employed to examine the performance of ECA* against genetic algorithm for clustering++ (GENCLUST++), learning vector quantisation (LVQ), expectation maximisation (EM), K-means++ (KM++) and K-means (KM). These algorithms are applied to 32 heterogenous and multi-featured datasets to determine which one performs well on the three tests. For one, ther paper examines the efficiency of ECA* in contradiction of its corresponding algorithms using clustering evaluation measures. These validation criteria are objective function and cluster quality measures. For another, it suggests a performance rating framework to measurethe the performance sensitivity of these algorithms on varos dataset features (cluster dimensionality, number of clusters, cluster overlap, cluster shape and cluster structure). The contributions of these experiments are two-folds: (i) ECA* exceeds its counterpart aloriths in ability to find out the right cluster number; (ii) ECA* is less sensitive towards dataset features compared to its competitive techniques. Nonetheless, the results of the experiments performed demonstrate some limitations in the ECA*: (i) ECA* is not fully applied based on the premise that no prior knowledge exists; (ii) Adapting and utilising ECA* on several real applications has not been achieved yet. |
| first_indexed | 2024-12-14T23:55:48Z |
| format | Article |
| id | doaj.art-2f21e8b58fa54f0589c2a584a3c6509b |
| institution | Directory Open Access Journal |
| issn | 2352-3409 |
| language | English |
| last_indexed | 2024-12-14T23:55:48Z |
| publishDate | 2021-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Data in Brief |
| spelling | doaj.art-2f21e8b58fa54f0589c2a584a3c6509b2022-12-21T22:43:08ZengElsevierData in Brief2352-34092021-06-0136107044Performance evaluation results of evolutionary clustering algorithm star for clustering heterogeneous datasetsBryar A. Hassan0Tarik A. Rashid1Seyedali Mirjalili2Kurdistan Institution for Strategic Studies and Scientific Research, Sulaimani, Iraq; Department of Computer Networks, Technical College of Informatics, Sulaimani Polytechnic University, Sulaimani, Iraq; Corresponding author at: Kurdistan Institution for Strategic Studies and Scientific Research, Sulaimani, Iraq.Computer Science and Engineering Department, University of Kurdistan Hewler, Erbil, IraqCentre for Artificial Intelligence Research and Optimisation, Torrens University, Australia; Yonsei Frontier Lab, Yonsei University, Seoul, KoreaThis article presents the data used to evaluate the performance of evolutionary clustering algorithm star (ECA*) compared to five traditional and modern clustering algorithms. Two experimental methods are employed to examine the performance of ECA* against genetic algorithm for clustering++ (GENCLUST++), learning vector quantisation (LVQ), expectation maximisation (EM), K-means++ (KM++) and K-means (KM). These algorithms are applied to 32 heterogenous and multi-featured datasets to determine which one performs well on the three tests. For one, ther paper examines the efficiency of ECA* in contradiction of its corresponding algorithms using clustering evaluation measures. These validation criteria are objective function and cluster quality measures. For another, it suggests a performance rating framework to measurethe the performance sensitivity of these algorithms on varos dataset features (cluster dimensionality, number of clusters, cluster overlap, cluster shape and cluster structure). The contributions of these experiments are two-folds: (i) ECA* exceeds its counterpart aloriths in ability to find out the right cluster number; (ii) ECA* is less sensitive towards dataset features compared to its competitive techniques. Nonetheless, the results of the experiments performed demonstrate some limitations in the ECA*: (i) ECA* is not fully applied based on the premise that no prior knowledge exists; (ii) Adapting and utilising ECA* on several real applications has not been achieved yet.http://www.sciencedirect.com/science/article/pii/S2352340921003280Evolutionary clustering algorithm starECA* performance evaluationECA* statistical performance evaluationECA* performance ranking framework |
| spellingShingle | Bryar A. Hassan Tarik A. Rashid Seyedali Mirjalili Performance evaluation results of evolutionary clustering algorithm star for clustering heterogeneous datasets Data in Brief Evolutionary clustering algorithm star ECA* performance evaluation ECA* statistical performance evaluation ECA* performance ranking framework |
| title | Performance evaluation results of evolutionary clustering algorithm star for clustering heterogeneous datasets |
| title_full | Performance evaluation results of evolutionary clustering algorithm star for clustering heterogeneous datasets |
| title_fullStr | Performance evaluation results of evolutionary clustering algorithm star for clustering heterogeneous datasets |
| title_full_unstemmed | Performance evaluation results of evolutionary clustering algorithm star for clustering heterogeneous datasets |
| title_short | Performance evaluation results of evolutionary clustering algorithm star for clustering heterogeneous datasets |
| title_sort | performance evaluation results of evolutionary clustering algorithm star for clustering heterogeneous datasets |
| topic | Evolutionary clustering algorithm star ECA* performance evaluation ECA* statistical performance evaluation ECA* performance ranking framework |
| url | http://www.sciencedirect.com/science/article/pii/S2352340921003280 |
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