CCE-Stream: Semi-supervised Stream Clustering Using Color-based Constraints

In recent years, Numerous stream clustering techniques have recently emerged. However these techniques do not utilize the valuable background knowledge provided by domain experts. Using knowledge in stream clustering offers several advantages, including enhanced accuracy and performance of the resulting clusters. The proposed method in this research is CCE-Stream, which incorporates background knowledge as constraints for incremental stream clustering. Instance-level constraints, including Must-Link and Cannot-Link constraints, are used to guide improved clustering behaviors in various operations. Constraint operators are introduced to handle evolving constraint characteristics. CCE-Stream introduces the concept of assigning colors to constraints and a new cluster representation called Color of Cluster (CoC). Experimental results on Covertype and Electricity datasets demonstrate increased F-measure and Purity.