Abstract Multi-Source Information Fusion (MSIF) is a comprehensive and interdisciplinary
subject, and is referred to as, multi-sensor information fusion which was originated in the …

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 …

Human pathophysiology is occasionally too complex for unaided hypothetical-deductive
reasoning and the isolated application of additive or linear statistical methods. Clustering …

Traditional partition-based clustering algorithms, hard or fuzzy version of C-means, could not
deal with high-dimensional data sets effectively as redundant features may impact the …

The objective of clustering ensemble is to find the underlying structure of data based on a
set of clustering results. It has been observed that the samples can change between clusters …

Ensemble clustering refers to learning a robust and accurate consensus result from a
collection of base clustering results. Despite extensive research on this topic, it remains …

For obtaining the more robust, novel, stable, and consistent clustering result, clustering
ensemble has been emerged. There are two approaches in clustering ensemble …