Clustering, as an unsupervised learning, is aimed at discovering the natural grou**s of a
set of patterns, points, or objects. In clustering algorithms, a significant problem is 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 …

Spiking neural networks (SNNs) are regarded as a promising candidate to deal with the
major challenges of current machine learning techniques, including the high energy …

Cluster ensemble has proved to be a good alternative when facing cluster analysis
problems. It consists of generating a set of clusterings from the same dataset and combining …

Computer and Machine Vision: Theory, Algorithms, Practicalities (previously entitled
Machine Vision) clearly and systematically presents the basic methodology of computer and …

Spectral clustering and path-based clustering are two recently developed clustering
approaches that have delivered impressive results in a number of challenging clustering …

Clustering ensembles have emerged as a powerful method for improving both the
robustness as well as the stability of unsupervised classification solutions. However, finding …

Many clustering algorithms, including cluster ensembles, rely on a random component.
Stability of the results across different runs is considered to be an asset of the algorithm. The …

Cluster ensembles have recently emerged as a powerful alternative to standard cluster
analysis, aggregating several input data clusterings to generate a single output clustering …

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 …