As a cornerstone of the world, uncertainty embodies the nature of data and knowledge. Existing uncertainty theory-based clustering algorithms learn fuzziness, i.e., the uncertainty of clustering objects belonging to different clusters. However, these algorithms do not refer to the fuzziness of objects themselves, i.e., the randomness of data. Here, we propose a clustering algorithm named Cloud-Cluster, which simultaneously characterizes the fuzziness and randomness of objects to reserve uncertain information, and to describe clusters into concepts. It embeds random uncertainty of concepts to extend the data distribution range for better data partitions and gradually constructs accurate concepts by an improved backward cloud transformation algorithm (MBCT-SR-Ex). Moreover, to ensure that the concept clustering process gradually converges, Cloud-Cluster introduces the Cluster Concept Drift Degree to evaluate the uncertainty of concepts during the clustering process. Experiments on UCI and OpenML clustering datasets show that Cloud-Cluster improves the average clustering accuracy by over 14% compared to K-Means and uncertainty theory-based clustering algorithms. Extensive experimental results on the evaluation of uncertainty show that Cloud-Cluster can handle the uncertainty of datasets in the clustering process well, in addition to exhibiting robustness with unclear clusters.
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