Abstract
As one of the important techniques of data analysis, a clustering algorithm is widely used in data mining, image recognition, information extraction, pattern recognition and other fields. In the era of big data, with the rapid development of web applications, much of the data to be processed is characterized by massive and dynamic growth. Under this background, how to cluster the incremental data becomes a challenging problem that clustering algorithms face. In this paper, we proposed a limited incremental clustering algorithm with respect to cluster stability. Based on an assumption that the categories of data are limited, we take advantage of an existing clustering structure and deal with incremental data steadily with respect to cluster stability. Cluster reconstruction will be triggered when the stability does no longer hold or a buffer pool for undetermined data is full. At the end of the paper, we implement the limited incremental clustering algorithm with K-means. Meanwhile, we use an average density of clusters and the global stability to choose a proper value of K.
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Acknowledgements
The work of the paper is partially supported by National Natural Science Foundation of China (No. 61303097) and Ph.D. Programs Foundation of Ministry of Education of China (No. 20123108120026).
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Zhu, W., Yao, W., Dai, S., Lu, Z. (2016). A Limited Incremental Clustering Algorithm with Respect to Cluster Stability. In: Xie, J., Chen, Z., Douglas, C., Zhang, W., Chen, Y. (eds) High Performance Computing and Applications. HPCA 2015. Lecture Notes in Computer Science(), vol 9576. Springer, Cham. https://doi.org/10.1007/978-3-319-32557-6_18
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DOI: https://doi.org/10.1007/978-3-319-32557-6_18
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