An Efficient K-Means Clustering Initialization Using Optimization Algorithm

• Published in: 2019 International Conference on Advances in Computing and Communication Engineering (ICACCE) pp. 1 - 7
• Main Authors: Divya, V., Deepika, R., Yamini, C., Sobiyaa, P.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.04.2019
• Subjects: Cuckoo Search initialization; Initial cluster centers; K-means algorithm
• DOI: 10.1109/ICACCE46606.2019.9079998

In data mining has a lot of technique for knowledge discovery. In this Clustering method is very well technique for unsupervised learning. It's important objective is to find a high-quality cluster where the distance between clusters are maximal and the distance in the cluster is minimal. K-means algorithm is applied in this paper for its simplicity. It has been widely discussed and applied in pattern recognition and machine learning. However, the K-means algorithm could not guarantee unique clustering results for the same dataset because its initial cluster centers are select randomly. To avoid such issues a new initialization method is proposed in the Improved K-means algorithm with Cuckoo Search algorithm. The proposed method uses different numerical datasets like iris, wine and solar datasets (Ames, Chariton stations). The K-means clustering solutions are comparable with cuckoo search initialization methods using different measures such as Accuracy, Precision and Recall, F1-score, Silhouette value and MSE (Mean Square Error). The experimental solution represents the effectiveness of the proposed method.