Curated by: Tao Li
The field of data mining increasingly adapts methods and algorithms from advanced matrix computations, graph theory and optimization. In these methods, the data is described using matrix representations (graphs are represented by their adjacency matrices) and the data mining problem is formulated as an optimization problem with matrix variables. With these, the data mining task becomes a process of minimizing or maximizing a desired objective function of matrix variables.
Prominent examples include spectral clustering, matrix factorization, tensor analysis, and regularizations. These matrix-formulated optimization-centric methodologies are rapidly evolving into a popular research area for solving challenging data mining problems. These methods are amenable to vigorous analysis and benefit from the well-established knowledge in linear algebra, graph theory, and optimization accumulated through centuries. They are also simple to implement and easy to understand, in comparison with probabilistic, information-theoretic, and other methods. In addition, they are well-suited to parallel and distributed processing for solving large scale problems. Last but not the least, these methodologies are quite flexible and they can be used to formulate a large number of data mining tasks.
Workshop on algorithms for modern massive datasets (MMDS)
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