Cluster ensembles have been shown to be better than any standard clustering algorithm at
improving accuracy and robustness across different data collections. This meta-learning …

Globally, olive (Olea europaea L.) productivity is threatened by plant pathogens, particularly
the fungus Verticillium dahliae (Vd) and the bacterium Xylella fastidiosa (Xf). Infections by …

A huge amount of image data is being collected in real world sectors. Image data analytics
provides information about important facts and issues of a particular domain. But, it is …

Traditional cluster ensemble approaches have several limitations.(1) Few make use of prior
knowledge provided by experts.(2) It is difficult to achieve good performance in high …

Встановлено, що відомі методи сегментування зображень не можуть бути напряму
застосовані до сегменту вання оптикоелектронних зображень бортових систем …

摘要聚类集成的目的是为了提高聚类结果的准确性, 稳定性和鲁棒性. 通过集成多个基聚类结果
可以产生一个较优的结果. 本文提出了一个基于密度峰值的聚类集成模型, 主要完成三个方面的 …

Spectral clustering techniques are valuable tools in signal processing and machine learning
for partitioning complex data sets. The effectiveness of spectral clustering stems from …

Crop planting area mapping is essential for crop phenology monitoring, yield prediction, and
disaster prevention. In this study, a winter wheat identification method combining Markov …

Cluster ensemble combines multiple base clustering results in a suitable way to improve the
accuracy of the clustering result. In the conventional cluster ensemble frameworks, pairwise …

Spectral clustering is able to extract clusters with various characteristics without a parametric
model, however it is infeasible for large datasets due to its high computational cost and …