Sparse representations with learned dictionaries have been successful in several image analysis applications. In this paper, we propose and analyze the framework of ensemble sparse models, and demonstrate their utility in image restoration and unsupervised clustering. The proposed ensemble model approximates the data as a linear combination of approximations from multiple weak sparse models. Th...

accurate quantitative precipitation forecasts (qpfs) have been always a demanding and challenging job in numerical weather prediction (nwp). the outputs of ensemble prediction systems (epss) in the form of probability forecasts provide a valuable tool for probabilistic quantitative precipitation forecasts (pqpfs). in this research, different configurations of wrf and mm5 meso-scale models form ...

Learning Object Repositories are increasingly being used in learning systems to provide high-quality, reusable educational materials. A relevant data mining problem associated with the automatic categorization of learning objects is the discovery of intrinsic classes based on the textual contents of the meta-data records. In this paper, we present a cluster ensemble method, that is applicable f...

As a powerful data analysis technique, clustering plays an important role in mining. Traditional hard uses one set with crisp boundary to represent cluster, which cannot solve the problem of inaccurate decision-making caused by information or insufficient data. In order this problem, three-way was presented show uncertainty dataset adding concept fringe region. paper, we present improved algori...

Unsupervised clustering plays a dominant role in detailed landcover identification specifically in agricultural and environmental monitoring of high spatial resolution remote sensing images. A method called Approximate Spectral Clustering enables spectral partitioning for big datasets to extract clusters with different characteristic without a parametric model. Various information types are use...

The recent years have witnessed a growing interest in two advanced strategies to cope with the data clustering problem, namely, clustering ensembles and multi-objective clustering. In this paper, we present a genetic programming based approach that can be considered as a hybrid of these strategies, thereby allowing that different hierarchical clustering ensembles be simultaneously evolved takin...