Abstract
Robust clustering on categorical sequences remains an open and challenging task due to the noise data and lack of an inherently meaningful measure of pairwise similarity between sequences. In this paper, a self-representation model is proposed as a representation of categorical sequences. Based on the model, we transform the robust clustering to a subspace clustering problem. Furthermore, an efficient algorithm for robust clustering of categorical sequences is also proposed, which provides the new measure with high-quality clustering results and the elimination of noise sequences using the subspace method. The experimental results on the synthetic and real world data demonstrate the promising performance of the proposed method.
Supported by the National Natural Science Foundation of China under Grant 61672157; Innovative Research Team of Probability and Statistics: Theory and Application (IRTL1704).
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Xu, K., Chen, L., Wang, S., Wang, B. (2018). A Self-representation Model for Robust Clustering of Categorical Sequences. In: U, L., Xie, H. (eds) Web and Big Data. APWeb-WAIM 2018. Lecture Notes in Computer Science(), vol 11268. Springer, Cham. https://doi.org/10.1007/978-3-030-01298-4_2
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DOI: https://doi.org/10.1007/978-3-030-01298-4_2
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