A novel evolving data stream clustering method based on optimization model
Liang BAI, Hangyuan Du, Wenjian WANG · Scientia Sinica Informationis · 2017
An optimization model based on the fuzzy maximum entropy method is proposed for the data stream evolving clustering problem. In the model, the fuzziness and effectiveness of cluster partition are described by fuzzy membership and information entropy, respectively. An optimization object function is defined. In the sliding window, the clustering processing of the data subset is construed as an optimization problem. In this way, the inner structural features can be depicted effectively, and the continuity between contiguous windows is preserved simultaneously. The solution of the optimization problem is used as the basis of concept drift detection; as a result, the validity of the detection result is guaranteed and the varying trends in cluster structure can be easily captured. In the simulation, artificial and real datasets are used to verify the performance of the proposed method, and existing evolving clustering algorithms are introduced for comparison with our algorithm for testing purposes. The simulation results demonstrate the validity of the developed algorithm. Under the same conditions, the new method is superior to other clustering algorithms with respect to the accuracy of clustering and concept drift detection; it also reduces computational load and memory usage effectively.