An algorithm for clustering heterogeneous data streams with uncertainty

In many applications, the heterogeneous data streams with uncertainty are ubiquitous. However, the clustering quality of the existing methods for clustering heterogeneous data streams with uncertainty is lower. In this paper, an algorithm for clustering heterogeneous data streams with uncertainty, called HU-Clustering, is proposed. A Heterogeneous Uncertainty Clustering Feature (H-UCF) is presented to describe the feature of heterogeneous data streams with uncertainty. Based on H-UCF, a probability frequency histogram is proposed to track the statistics of categorical attributes; the algorithm initially creates n clusters by k-prototypes algorithm. In order to improve clustering quality, a two phase streams clustering selection process is applied to HU-Clustering algorithm. Firstly, the candidate clustering is selected through the new similarity measure; secondly, the most similar cluster for each new arriving tuple is selected through clustering uncertainty in candidate clustering set. The experimental results show that the clustering quality of HU-Clustering is higher than that of UMicro.