In many modern applications, the generated data is a dynamic networks. The networks are graphs that change over time by a sequence of update operations (node addition, node deletion, edge addition, edge deletion, and ...
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In many modern applications, the generated data is a dynamic networks. The networks are graphs that change over time by a sequence of update operations (node addition, node deletion, edge addition, edge deletion, and edge weight change). In these networks, it is inefficient to compute from scratch the solution of a data mining/machine learning task, after any update operation. Therefore in recent years, several so-called dynamical algorithms have been proposed that update the solution, instead of computing it from scratch. In this paper, first we formulate this emerging setting and discuss its high-level algorithmic aspects. Then, we review state of the art dynamical algorithms proposed for several data mining and machine learning tasks, including frequent pattern discovery, betweenness/closeness/PageRank centralities, clustering, classification, and regression. This article is categorized under: Technologies > Structure Discovery and Clustering Technologies > Machine Learning Fundamental Concepts of Data and Knowledge > Big Data Mining
The Bullard dynamo and the Rikitake system are two well-known non-linear systems, which are used to capture the main aspects of the magnetic field of the Earth, especially those non-linear and chaotic. Aim of this stu...
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The Bullard dynamo and the Rikitake system are two well-known non-linear systems, which are used to capture the main aspects of the magnetic field of the Earth, especially those non-linear and chaotic. Aim of this study is two-fold: the review, on the basis of the modern differential geometry, of the dynamical aspects of both systems, and the design, on the basis of the modern geometric control theory, of dynamical algorithms for the estimation of the state variables, which is a challenging problem because of the chaotic behaviour presented by both systems. All the analyses are validated in simulation.
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