Abstract
Due to the evolving nature of temporal data, clusters often exhibit complex dynamic patterns like birth and death. In particular, a cluster can branch into multiple clusters simultaneously. Intuitively, clusters can evolve as evolutionary trees over time. However, existing models are incapable of recovering the tree-like evolutionary trace in temporal data. To this end, we propose an Evolving Chinese Restaurant Process (ECRP), which is essentially a temporal non-parametric clustering model. ECRP incorporates dynamics of cluster number, parameters and popularity. ECRP allows each cluster to have multiple branches over time. We design an online learning framework based on Gibbs sampling to infer the evolutionary traces of clusters over time. In experiments, we validate that ECRP can capture tree-like evolutionary traces of clusters from real-world data sets and achieve better clustering results than the state-of-the-art methods.
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Wang, P., Zhou, C., Zhang, P., Feng, W., Guo, L., Fang, B. (2015). Evolving Chinese Restaurant Processes for Modeling Evolutionary Traces in Temporal Data. In: Cao, T., Lim, EP., Zhou, ZH., Ho, TB., Cheung, D., Motoda, H. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2015. Lecture Notes in Computer Science(), vol 9078. Springer, Cham. https://doi.org/10.1007/978-3-319-18032-8_7
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DOI: https://doi.org/10.1007/978-3-319-18032-8_7
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