@article{1434631,
      author = {Aljarah, Ibrahim, and Faris, Hossam, and Mirjalili,  Seyedali,},
      url = {http://library.usi.edu/record/1434631},
      title = {Evolutionary data clustering : algorithms and applications  /},
      abstract = {This book provides an in-depth analysis of the current  evolutionary clustering techniques. It discusses the most  highly regarded methods for data clustering. The book  provides literature reviews about single objective and  multi-objective evolutionary clustering algorithms. In  addition, the book provides a comprehensive review of the  fitness functions and evaluation measures that are used in  most of evolutionary clustering algorithms. Furthermore, it  provides a conceptual analysis including definition,  validation and quality measures, applications, and  implementations for data clustering using classical and  modern nature-inspired techniques. It features a range of  proven and recent nature-inspired algorithms used to data  clustering, including particle swarm optimization, ant  colony optimization, grey wolf optimizer, salp swarm  algorithm, multi-verse optimizer, Harris hawks  optimization, beta-hill climbing optimization. The book  also covers applications of evolutionary data clustering in  diverse fields such as image segmentation, medical  applications, and pavement infrastructure asset  management.},
      doi = {https://doi.org/10.1007/978-981-33-4191-3},
      recid = {1434631},
      pages = {1 online resource (xii, 248 pages) :},
}