| Abstrakt: |
As a classical clustering algorithm, DBSCAN algorithm is widely used in various fields. However, due to the poor adaptability of its parameters, the application effect depends entirely on the setting of parameters. Based on this, an adaptive DBSCAN algorithm based on improved harmony search is proposed to improve the adaptability of DBSCAN algorithm. The algorithm first uses the K-means nearest neighbor algorithm to optimize the initial population, thereby improving the quality of the initial population and providing a high-quality solution for subsequent evolutionary calculations. Secondly, an update operator based on double difference is designed to improve the search ability of the algorithm. Thirdly, two update strategy structures are used to avoid premature convergence of the algorithm, improve the optimization ability of the harmony search algorithm, and comprehensively improve the adaptability of the DBSCAN algorithm. Finally, a variety of datasets are used and comparative experiments are designed to verify the proposed algorithm. Experimental results show that the proposed algorithm has better recognition ability and adaptability. [ABSTRACT FROM AUTHOR] |
