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 …

This paper focuses on scalability and robustness of spectral clustering for extremely large-
scale datasets with limited resources. Two novel algorithms are proposed, namely, ultra …

Due to its ability to combine multiple base clusterings into a probably better and more robust
clustering, the ensemble clustering technique has been attracting increasing attention in …

Ensemble clustering has been a popular research topic in data mining and machine
learning. Despite its significant progress in recent years, there are still two challenging …

As a promising way for heterogeneous data analytics, consensus clustering has attracted
increasing attention in recent decades. Among various excellent solutions, the co …

Clustering ensemble, or consensus clustering, has emerged as a powerful tool for improving
both the robustness and the stability of results from individual clustering methods. Weighted …

In recent years, internet technologies and its rapid growth have created a paradigm of digital
services. In this new digital world, users suffer due to the information overload problem and …

Although many successful ensemble clustering approaches have been developed in recent
years, there are still two limitations to most of the existing approaches. First, they mostly …

In this paper, we propose a new ensemble clustering approach termed ensemble clustering
using factor graph (ECFG). Compared to the existing approaches, our approach has three …

The development of internet technologies has brought digital services to the hands of
common man. In the selection process of relevant digital services to the active target user …