WBBA-KM: A Hybrid Weight-Based Bat Algorithm with K-Means Algorithm For Cluster Analysis

Data clustering is an unsupervised classification method used to classify unlabeled objects into clusters. The clustering is performed by partitioning clustering, hierarchical clustering, fuzzy clustering, and density-based clustering methods. However, the center of the clusters is updated according to local searches with these traditional methods, and finding the best clusters center affects the clustering performance positively. In this study, a variant bat algorithm called weight-based bat algorithm (WBBA) is proposed and the proposed WBBA hybridized with the k-means clustering method (WBBA-KM) to determine the optimal centers of the clusters. The performance of the proposed WBBA-KM has been evaluated by using six different benchmark datasets from the UCI repository and the obtained results are compared with FCM, IFCM, KFCM, KIFCM, PSO-IFCM, GA-IFCM, ABC-IFCM, PSO-KIFCM, GA-KIFCM, ABC-KIFCM, and BA-KM clustering methods in the literature. According to the experimental results, the proposed WBBA-KM clustering method performed better performance from all other clustering methods in 4 of 6 benchmark datasets and achieved better performance from the BA-KM clustering method in all benchmark datasets.

WBBA-KM: A Hybrid Weight-Based Bat Algorithm with K-Means Algorithm For Cluster Analysis

Data clustering is an unsupervised classification method used to classify unlabeled objects into clusters. The clustering is performed by partitioning clustering, hierarchical clustering, fuzzy clustering, and density-based clustering methods. However, the center of the clusters is updated according to local searches with these traditional methods, and finding the best clusters center affects the clustering performance positively. In this study, a variant bat algorithm called weight-based bat algorithm (WBBA) is proposed and the proposed WBBA hybridized with the k-means clustering method (WBBA-KM) to determine the optimal centers of the clusters. The performance of the proposed WBBA-KM has been evaluated by using six different benchmark datasets from the UCI repository and the obtained results are compared with FCM, IFCM, KFCM, KIFCM, PSO-IFCM, GA-IFCM, ABC-IFCM, PSO-KIFCM, GA-KIFCM, ABC-KIFCM, and BA-KM clustering methods in the literature. According to the experimental results, the proposed WBBA-KM clustering method performed better performance from all other clustering methods in 4 of 6 benchmark datasets and achieved better performance from the BA-KM clustering method in all benchmark datasets.

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Bibtex @araştırma makalesi { politeknik689384, journal = {Politeknik Dergisi}, eissn = {2147-9429}, address = {Gazi Üniversitesi Teknoloji Fakültesi 06500 Teknikokullar - ANKARA}, publisher = {Gazi Üniversitesi}, year = {2022}, volume = {25}, number = {1}, pages = {65 - 73}, doi = {10.2339/politeknik.689384}, title = {WBBA-KM: A Hybrid Weight-Based Bat Algorithm with K-Means Algorithm For Cluster Analysis}, key = {cite}, author = {Ibrahım, Mohammed Hussein} }
APA Ibrahım, M. H. (2022). WBBA-KM: A Hybrid Weight-Based Bat Algorithm with K-Means Algorithm For Cluster Analysis . Politeknik Dergisi , 25 (1) , 65-73 . DOI: 10.2339/politeknik.689384
MLA Ibrahım, M. H. "WBBA-KM: A Hybrid Weight-Based Bat Algorithm with K-Means Algorithm For Cluster Analysis" . Politeknik Dergisi 25 (2022 ): 65-73 <
Chicago Ibrahım, M. H. "WBBA-KM: A Hybrid Weight-Based Bat Algorithm with K-Means Algorithm For Cluster Analysis". Politeknik Dergisi 25 (2022 ): 65-73
RIS TY - JOUR T1 - WBBA-KM: A Hybrid Weight-Based Bat Algorithm with K-Means Algorithm For Cluster Analysis AU - Mohammed Hussein Ibrahım Y1 - 2022 PY - 2022 N1 - doi: 10.2339/politeknik.689384 DO - 10.2339/politeknik.689384 T2 - Politeknik Dergisi JF - Journal JO - JOR SP - 65 EP - 73 VL - 25 IS - 1 SN - -2147-9429 M3 - doi: 10.2339/politeknik.689384 UR - Y2 - 2020 ER -
EndNote %0 Politeknik Dergisi WBBA-KM: A Hybrid Weight-Based Bat Algorithm with K-Means Algorithm For Cluster Analysis %A Mohammed Hussein Ibrahım %T WBBA-KM: A Hybrid Weight-Based Bat Algorithm with K-Means Algorithm For Cluster Analysis %D 2022 %J Politeknik Dergisi %P -2147-9429 %V 25 %N 1 %R doi: 10.2339/politeknik.689384 %U 10.2339/politeknik.689384
ISNAD Ibrahım, Mohammed Hussein . "WBBA-KM: A Hybrid Weight-Based Bat Algorithm with K-Means Algorithm For Cluster Analysis". Politeknik Dergisi 25 / 1 (Mart 2022): 65-73 .
AMA Ibrahım M. H. WBBA-KM: A Hybrid Weight-Based Bat Algorithm with K-Means Algorithm For Cluster Analysis. Politeknik Dergisi. 2022; 25(1): 65-73.
Vancouver Ibrahım M. H. WBBA-KM: A Hybrid Weight-Based Bat Algorithm with K-Means Algorithm For Cluster Analysis. Politeknik Dergisi. 2022; 25(1): 65-73.
IEEE M. H. Ibrahım , "WBBA-KM: A Hybrid Weight-Based Bat Algorithm with K-Means Algorithm For Cluster Analysis", Politeknik Dergisi, c. 25, sayı. 1, ss. 65-73, Mar. 2022, doi:10.2339/politeknik.689384