Application of the novel harmony search optimization algorithm for DBSCAN clustering

• Published in: Expert systems with applications Vol. 178; p. 115054
• Main Authors: Zhu, Qidan, Tang, Xiangmeng, Elahi, Ahsan
• Format: Journal Article
• Language: English
• Published: New York Elsevier Ltd 15.09.2021; Elsevier BV
• Subjects: Algorithms; Clustering; DBSCAN; K-DBSCAN; Novel harmony search; Optimization; Optimization algorithms; Parameters; Searching; DBSCAN; Novel harmony search; Clustering; K-DBSCAN
• ISSN: 0957-4174; 1873-6793
• DOI: 10.1016/j.eswa.2021.115054

•Propose the K-DBSCAN clustering method, which can get K clusters of arbitrary shapes.•The novel harmony search is presented to optimize the clustering parameters.•Apply the novel harmony search to DBSCAN to realize the K-DBSCAN clustering. At present, the DBSCAN clustering algorithm has been commonly used principally due to its ability in discovering clusters with arbitrary shapes. When the cluster number K is predefined, though the partitional clustering methods can perform efficiently, they cannot process the non-convex clustering and easily fall into local optimum. Thereby the concept of K-DBSCAN clustering is proposed in this paper. But the basic DBSCAN has a crucial defect, that is, difficult to predict the suitable clustering parameters. Here, the well-known harmony search (HS) optimization algorithm is considered to deal with this problem. By modifying the original HS, the novel harmony search (novel-HS) is put forward, which can improve the accuracy of results as well as enhance the robustness of optimization. In K-DBSCAN, the novel-HS is used to optimize the clustering parameters of DBSCAN to obtain better clustering effect with the number of K classifications. Experimental results show that the designed clustering method has superior performance to others and can be successfully considered as a new clustering scheme for further research.