Concept Clustering of Evolving Data

Much work has focused on mining evolving data, and most approaches learn the latest model from the latest data. The problem with these approaches is that the learned model is always of low quality. In this paper, we propose a clustering approach to find hidden concepts that control data generation. Unlike traditional clustering methods that are based on data similarity (measured by Euclidean distance, e.g.), we devise a new similarity metric for concept similarity. We propose a two step algorithm, which uses dynamic programming and hierarchical clustering to find concepts in the data.