title: Unsupervised Tensor Mining for Big Data Practitioners.
abstract: Multiaspect data are ubiquitous in modern Big Data applications. For instance, different aspects of a social network are the different types of communication between people, the time stamp of each interaction, and the location associated to each individual. How can we jointly model all those aspects and leverage the additional information that they introduce to our analysis? Tensors, which are multidimensional extensions of matrices, are a principled and mathematically sound way of modeling such multiaspect data. In this article, our goal is to popularize tensors and tensor decompositions to Big Data practitioners by demonstrating their effectiveness, outlining challenges that pertain to their application in Big Data scenarios, and presenting our recent work that tackles those challenges. We view this work as a step toward a fully automated, unsupervised tensor mining tool that can be easily and broadly adopted by practitioners in academia and industry.
authors.affiliation: 1 Department of Computer Science, Carnegie Mellon University , Pittsburgh, Pennsylvania.
authors.forename: Evangelos E
authors.initials: EE
authors.lastname: Papalexakis
authors.affiliation: 1 Department of Computer Science, Carnegie Mellon University , Pittsburgh, Pennsylvania.
authors.forename: Christos
authors.initials: C
authors.lastname: Faloutsos
mesh_terms: D003198:Computer Simulation; D057225:Data Mining; D000069550:Machine Learning
keywords: big data analytics; data mining; machine learning