Clustering Heterogeneous Data Streams with Uncertainty over Sliding Window

Existing methods for clustering uncertain data streams over sliding windows do not treat the categorical attributes. However, uncertain mixed data are ubiquitous. This paper investigates the problem of clustering heterogeneous data streams pervaded by uncertainty over sliding windows, so-called SWHU-Clustering. A Heterogeneous Uncertain Temporal Cluster Feature (HUTCF) is introduced to monitor the distribution statistics of mixed data points. Based on this structure, Exponential Histogram of Heterogeneous Uncertain Cluster Feature (EHHUCF) is presented as a collection of HUTCF. This structure may help to handle the in-cluster evolution, and detects the temporal change of the cluster distribution. Our approach has several advantages over existing method: 1) the higher execution efficiency benefits from its good design as it avoids the effects of old data on the final results. 2) We incorporated the k-NN into the clustering process in order to reduce the complexity of the algorithm. 3) Memory consumption can be managed efficiently by limiting the number of HUTCF in each EHHUCF. Simulations on real databases show the feasibility of SWHU-Clustering as well as its effectiveness by comparing it with UMicro algorithm.