Abstract
In many real-world application scenarios, data usually incrementally update over time. In such cases, traditional constrained clustering algorithms become unsuitable for dealing with incremental data because of high computational cost. In this paper, we propose a novel incremental constrained random walk clustering algorithm (ICC), which not only efficiently deal with the incremental data but also utilize the incremental constraints. To reduce the time complexity, it updates the influence range of each selected data point and utilizes the intermediate structure of the previous time step. Extensive experiment results on datasets demonstrate that our algorithm is both effective and efficient.
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Acknowledgements
This research was supported in part by the Chinese National Natural Science Foundation under Grant Nos. 61402395, 61472343, 61379066, and 61502412, Natural Science Foundation of Jiangsu Province under contracts BK20140492, BK20151314, and BK20150459, Jiangsu overseas research and training program for university prominent young and middle-aged teachers and presidents, Jiangsu government scholarship funding, and practice innovation plan for college graduates of Jiangsu Province under contracts SJLX16_0591.
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He, P., Jing, T., Xu, X., Lin, H., Liao, Z., Fan, B. (2019). Incremental Constrained Random Walk Clustering. In: Bhatia, S., Tiwari, S., Mishra, K., Trivedi, M. (eds) Advances in Computer Communication and Computational Sciences. Advances in Intelligent Systems and Computing, vol 760. Springer, Singapore. https://doi.org/10.1007/978-981-13-0344-9_21
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DOI: https://doi.org/10.1007/978-981-13-0344-9_21
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