Unsupervised learning via iteratively constructed clustering ensemble

Unsupervised classification or clustering is an important data analysis technique demanded in various fields including machine learning, data mining, pattern recognition, image analysis and bioinformatics. Recently a large number of studies have attempted to improve clustering by combing multiple clustering solutions into a single consolidated clustering ensemble that has the best performance among given clustering solutions. However, the different clustering ensembles have their own behaviors on data of various characteristics. In this paper, we propose a novel approach to data clustering by constructing a clustering ensemble iteratively based on partitions generated on training subsets sampled from the original dataset. To yield a robust clustering ensemble our approach employs a hybrid sampling scheme inspired by both boosting and bagging techniques originally proposed for supervised learning. Our approach has been evaluated on synthetic data and real-world motion trajectory data sets, and experimental results demonstrate that it yields satisfactory performance for a variety of clustering tasks.