The performance of decomposition-based algorithms is sensitive to the Pareto front shapes
since their reference vectors preset in advance are not always adaptable to various problem …

Ensemble clustering is aimed at obtaining a robust consensus result from a set of weak base
clusterings. Most existing methods rely on a co-association (CA) matrix that describes the …

As one of the essential topics in ensemble learning, a clustering ensemble is employed to
aggregate multiple base patterns to generate a single clustering output for improving …

Link prediction tasks focus on predicting possible future connections. Most existing
researches measure the likelihood of links by different similarity scores on node pairs and …

Currently, feature selection faces a huge challenge that no single feature selection method
can effectively deal with various data sets for all real cases. Ensemble learning is a potential …

Consensus clustering is gaining increasing attention for its high quality and robustness. In
particular, k-means-based Consensus Clustering (KCC) converts the usual computationally …

Ensemble clustering has shown its promising ability in fusing multiple base clusterings into a
probably better and more robust clustering result. Typically, the co-association matrix based …

The Accurate segmentation and classification takes place a major role in the medical image
processing to detect and locate the abnormal tissue region. In this, the three different types …

Patient stratification has been studied widely to tackle subtype diagnosis problems for
effective treatment. Due to the dimensionality curse and poor interpretability of data, there is …

Since positive data vectors are often naturally generated in various real-life applications,
positive vectors modeling has become an important research topic. In this article, we tackle …