An Efficient K-Means Clustering Initialization Using Optimization Algorithm
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-mea...
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Published in | 2019 International Conference on Advances in Computing and Communication Engineering (ICACCE) pp. 1 - 7 |
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Main Authors | , , , |
Format | Conference Proceeding |
Language | English |
Published |
IEEE
01.04.2019
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Subjects | |
Online Access | Get full text |
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Summary: | 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. |
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DOI: | 10.1109/ICACCE46606.2019.9079998 |