Algorithms and Applications for Dimensionality Reduction, Similarity Metric, and Clustering of High Multidimensional Vectors and Tensors

Dear Colleagues,

For the last few decades, exponential increases in data generation have been ongoing as users adopt to applications on the internet and mobile networks, like e-commerce and social networks. The huge data, collected from users’ past actions, is a gold mine for improving business and services provided to them. The same is true for data collected through sensor networks and in many other fields of research: bioinformatics, health and medical informatics, and text mining, to name just a few.

The core step in data analysis and mining is to cluster data vectors, for which it is necessary to formulate a meaningful similarity metric between a pair of vectors. For many applications, the data vectors are of very high dimension, noisy, and often sparse. Occasionally, tensors are suitable for the representation of multidimensional data. In addition to conventional tools for dimensionality reduction and distance/similarity metrics, a plethora of algorithms which learn the most suitable similarity measure from the data itself have recently been proposed and successfully used for different applications. Non-linear dimensionality reduction techniques show better results for a large class of data.

There are many clustering algorithms available. Much of the big data we deal with, for example data generated from social network services or gene expression data, are structured. Network (graphical) presentation and community detection is an effective and efficient way to divulge the underlying structure in the data.

In light of all the present developments and new applications, the aim of this Special Issue is to examine the state-of-the-art algorithms for similarity measurements, clustering methods, novel ideas and improvements of the existing ones, and their efficacies in different applications. In addition to new unpublished works, review papers are also welcome.

Prof. Dr. Goutam Chakraborty
Prof. Dr. Wen-Liang Hwang
Guest Editors

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• High dimensión data vector
• Tensor data
• Sparse data
• Similarity/Distance metric for high dimensión data
• Dimension Reduction
• Clustering
• Community detection in networks