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Erschienen in:
2018 | OriginalPaper | Buchkapitel
A Large-Scale Data Clustering Algorithm Based on BIRCH and Artificial Immune Network
verfasst von : Yangyang Li, Guangyuan Liu, Peidao Li, Licheng Jiao
Erschienen in: Advances in Swarm Intelligence
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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.