An Improved Gray Wolf Optimization (IGWO) and its Linkage to K-Mean for using in Data Clustering
DOI:
https://doi.org/10.61841/gj0gt408Keywords:
Data Clustering, meta-heuristic algorithm, Gray Wolf Optimization (GWO), k-meanAbstract
The k-means algorithm remains one of the most well-known and widespread clustering algorithms, whose initial centers are chosen randomly, and while being optimally placed, their application can be easily implemented. The meta-heuristic algorithm can provide data clustering with the optimal solution. This algorithm can also minimize the issue of local minimums. The present study aims at improving the k-means algorithm’s accuracy with the use of combined and meta-heuristic techniques. Hence, this paper addresses an algorithm (Improved Gray Wolf Optimization K-mean). The optimized form of improved gray wolf is employed for automatically detecting the clusters’ number and obtaining the optimal solution as K-mean clustering outcomes and initial K-mean clustering centers. The results revealed that the proposed method bears a lower percentage of error compared to the existing methods and reduces it by 12%. Additionally, the aggregate distance of intra-cluster was also decreased.
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