Big Data Analysis of Complex Networks Using Machine Learning Methods



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With the tremendous development of the modern complex networks such as smart grid and wireless communication domains, the data analysis tasks are significantly involved. In smart grid systems, there are emerging concerns on recognizing energy users' behavior patterns so that the energy trading companies are able to provide customized services. To understand users' usage patterns, efficient grouping methods are required such as clustering or nonparametric Bayesian models in machine learning field. In wireless communication field, a heat topic of locating personal devices and trajectory analysis is drawing more and more attention with the development of advanced personal devices such as the smart phones.

Given this background, this dissertation provides a theoretical research in smart grid systems and wireless communications networks with emphases on probabilistic clustering analysis, pricing scheme design, sublinear sampling, tensor voting theory and trajectory pattern recognition. The main contributions of this dissertation include: a comprehensive overview of basic concepts, models and state-of-the-art techniques used in smart grid and trajectory analysis is provided; a novel distance measurement for clustering analysis is proposed from the probabilistic point of view. Moreover, the stopping rules and clustering quality problems have been investigated with proposed novel metrics; the pricing schemes design has been formulated into an optimization problem. The novel sublinear sampling algorithm has been developed to address the computation efficiency in the context of big data; the tensor voting theory has been introduced to the trajectory inference problem and is implemented in the sparse sense to facilitate the computation. The fractal analysis has been employed as a novel method to extract trajectory features for trajectory pattern recognition tasks.



Big data, Machine learning, Networking, Trajectory, Smart grids, Clustering, Sublinear sampling, Tensor voting


Portions of this document have appeared in: Pan, Erte, Miao Pan, and Zhu Han. "Tensor Voting Techniques and Applications in Mobile Trace Inference." IEEE Access 3 (2015): 3000-3009. DOI: 10.1109/ACCESS.2015.2512380.