Performance evaluation results of evolutionary clustering algorithm star for clustering heterogeneous datasets
Kurdistan Institution for Strategic Studies and Scientific Research（库尔德斯坦战略研究与科学研究所）；University of Kurdistan Hewler（库尔德斯坦赫勒大学）；Sulaimani Polytechnic University（苏莱曼尼亚理工大学）；Torrens University（托伦斯大学）；Yonsei University（延世大学）
Bryar A. Hassan, TarikA. Rashid, Seyedali Mirjalili
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.
Clustering-based Transfer Learning for Dynamic Multimodal MultiObjective Evolutionary Algorithm
School of Automation and Electrical Engineering, Zhongyuan University of Technology（中原文理大学自动化与电气工程学院）；School of Electrical Engineering, Zhengzhou University（郑州大学电气工程学院）
Clustering in Dynamic Environments: A Framework for Benchmark Dataset Generation With Heterogeneous Changes
Faculty of Engineering & Information Technology, University of Technology Sydney（悉尼科技大学工程与信息技术学院）；Warwick Business school, University of Warwick（华威大学华威商学院）；School of Computing and Leeds University Business School, University of Leeds（利兹大学计算学院与利兹大学商学院）；RMIT University（皇家墨尔本理工大学）；University Research and Innovation Center (EKIK), Obuda University（奥布达大学大学研究与创新中心）；Lingnan University（岭南大学）；CERCIA, School of Computer Science, University of Birmingham（伯明翰大学计算机科学学院CERCIA研究中心）
A Multi-disciplinary Ensemble Algorithm for Clustering Heterogeneous Datasets
Kurdistan Institution for Strategic Studies and Scientific Research（库尔德斯坦战略研究与科学研究机构）；University of Kurdistan Hewler（库尔德斯坦赫勒大学）；Sulaimani Polytechnic University（苏莱曼尼理工大学）
Scaling Multi-Objective Security Games Provably via Space Discretization Based Evolutionary Search
East China Normal University（华东师范大学）；Shanghai Institute of AI for Education, East China Normal University（华东师范大学上海人工智能教育研究院）；Nanjing University（南京大学）；Polixir Technologies（Polixir科技）；Wenzhou University（温州大学）
