Notice Board :

Call for Paper
Vol. 14 Issue 4

Submission Start Date:
May 01, 2022

Acceptence Notification Start:
May 10, 2022

Submission End:
May 20, 2022

Final MenuScript Due:
May 22, 2022

Publication Date:
May 25, 2021
                         Notice Board: Call for PaperVol. 14 Issue 4      Submission Start Date: May 01, 2022      Acceptence Notification Start: May 10, 2022      Submission End: May 20, 2022      Final MenuScript Due: May 22, 2022      Publication Date: May 25, 2021




Volume XIII Issue XI

Author Name
Kanishka Sisodia, Chinmay Bhatt, Varsha Namdeo
Year Of Publication
2021
Volume and Issue
Volume 13 Issue 11
Abstract
Traditional K-means clustering algorithms have the drawback of getting stuck at local optima that depend on the random values of initial centroids. Optimization algorithms have their advantages in guiding iterative computation to search for global optima while avoiding local optima. The algorithms help speed up the clustering process by converging into a global optimum early with multiple search agents in action. Inspired by nature, some contemporary optimization algorithms which include Ant, Bat, Cuckoo, Firefly, and Wolf search algorithms mimic the swarming behavior allowing them to cooperatively steer towards an optimal objective within a reasonable time. It is known that these so-called nature-inspired optimization algorithms have their own characteristics as well as pros and cons in different applications. When these algorithms are combined with K-means clustering mechanism for the sake of enhancing its clustering quality by avoiding local optima and finding global optima,
PaperID
2021/EUSRM/11/2021/61246