Multimodal classification research has been gaining popularity with new datasets in
domains such as satellite imagery, biometrics, and medicine. Prior research has shown the …

Biclustering is an unsupervised machine-learning technique that simultaneously clusters
rows and columns in a data matrix. Over the past two decades, the field of biclustering has …

Multi-view subspace clustering approaches have shown outstanding performance in
revealing similarity relationships and complex structures hidden in data. Despite the …

We present CVIK, a Matlab-based toolbox for assisting the process of cluster analysis
applications. This toolbox aims to implement 28 cluster validity indices (CVIs) for measuring …

Multiview data has become very important because it is often possible to obtain multiple
representations for the same set of objects. From the perspective of soft partition, this paper …

The present study evaluated the capability of the hyperspectral imaging system (HSI) as a
rapid and non-destructive technique to identify the authenticity and origin of three Iranian …

This paper introduces a novel similarity function that evaluates both the quantitative and
qualitative similarities between data instances, named QQ-Means (Qualitative and …

This chapter provides a formal definition of the problem of cluster analysis, and the related
problem of community detection in graphs. Building on the mathematical definition of these …

Multiview data analysis provides an effective means to integrate the distinct information
sources which are inherent to many applications. Data clustering in a multiview setting …

Biclustering is an unsupervised machine learning technique that simultaneously clusters
rows and columns in a data matrix. Biclustering has emerged as an important approach and …