In various disciplines, information about the same phenomenon can be acquired from
different types of detectors, at different conditions, in multiple experiments or subjects …

Traditional spectral-based methods such as PCA are popular for anomaly detection in a
variety of problems and domains. However, if data includes tensor (multiway) structure (eg …

Tensors and tensor decompositions are very powerful and versatile tools that can model a
wide variety of heterogeneous, multiaspect data. As a result, tensor decompositions, which …

Tensor completion is a problem of filling the missing or unobserved entries of partially
observed tensors. Due to the multidimensional character of tensors in describing complex …

Computational phenotyping is the process of converting heterogeneous electronic health
records (EHRs) into meaningful clinical concepts. Unsupervised phenotyping methods have …

We present structured data fusion (SDF) as a framework for the rapid prototyping of
knowledge discovery in one or more possibly incomplete data sets. In SDF, each data set …

A substantial progress in development of new and efficient tensor factorization techniques
has led to an extensive research of their applicability in recommender systems field. Tensor …

As one important technique of fuzzy clustering in data mining and pattern recognition, the
possibilistic c-means algorithm (PCM) has been widely used in image analysis and …

Link prediction is an important task in data mining, which has widespread applications in
social network research. Given a social network, the objective of this task is to predict future …

With a goal of identifying biomarkers/patterns related to certain conditions or diseases,
metabolomics focuses on the detection of chemical substances in biological samples such …