Abstract
This paper describes a large-scale data clustering algorithm which is a combination of Balanced Iterative Reducing and Clustering using Hierarchies Algorithm (BIRCH) and Artificial Immune Network Clustering Algorithm (aiNet). Compared with traditional clustering algorithms, aiNet can better adapt to non-convex datasets and does not require a given number of clusters. But it is not suitable for handling large-scale datasets for it needs a long time to evolve. Besides, the aiNet model is very sensitive to noise, which greatly restricts its application. Contrary to aiNet, BIRCH can better process large-scale datasets but cannot deal with non-convex datasets like traditional clustering algorithms, and requires the cluster number. By combining these two methods, a new large-scale data clustering algorithm is obtained which inherits the advantages and overcomes the disadvantages of BIRCH and aiNet simultaneously.
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Acknowledgment
This work was supported by the National Natural Science Foundation of China under Grant 61772399, Grant U170126, Grant 61773304, Grant 61672405 and Grant 61772-400, the Program for Cheung Kong Scholars and Innovative Research Team in University Grant IRT_15R53, the Fund for Foreign Scholars in University Research and Teaching Programs (the 111 Project) Grant B07048, and the Major Research Plan of the National Natural Science Foundation of China Grant 91438201.
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Li, Y., Liu, G., Li, P., Jiao, L. (2018). A Large-Scale Data Clustering Algorithm Based on BIRCH and Artificial Immune Network. In: Tan, Y., Shi, Y., Tang, Q. (eds) Advances in Swarm Intelligence. ICSI 2018. Lecture Notes in Computer Science(), vol 10941. Springer, Cham. https://doi.org/10.1007/978-3-319-93815-8_32
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