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      •   BUIR Home
      • University Library
      • Bilkent Theses
      • Theses - Department of Computer Engineering
      • Dept. of Computer Engineering - Ph.D. / Sc.D.
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      Relevance feedback and sparsity handling methods for temporal data

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      Author
      Eravcı, Bahaeddin
      Advisor
      Ulusoy, Özgür
      Date
      2018-08
      Publisher
      Bilkent University
      Language
      English
      Type
      Thesis
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      Abstract
      Data with temporal ordering arises in many natural and digital processes with an increasing importance and immense number of applications. This study provides solutions to data mining problems in analyzing time series both in standalone and sparse networked cases. We initially develop a methodology for browsing time series repositories by forming new time series queries based on user annotations. The result set for each query is formed using diverse selection methods to increase the effectiveness of the relevance feedback (RF) mechanism. In addition to RF, a unique aspect of time series data is considered and representation feedback methods are proposed to converge to the outperforming representation type among various transformations based on user annotations as opposed to manual selection. These methods are based on partitioning of the result set according to representation performance and a weighting approach which amplifies different features from multiple representations. We subsequently propose the utilization of autoencoders to summarize the time series into a data-aware sparse representation to both decrease computation load and increase the accuracy. Experiments on a large variety of real data sets prove that the proposed methods improve the accuracy significantly and data-aware representations have recorded similar performances while reducing the data and computational load. As a more demanding case, the time series dataset may be incomplete needing interpolation approaches to apply data mining techniques. In this regard, we analyze a sparse time series data with an underlying time varying network. We develop a methodology to generate a road network time series dataset using noisy and sparse vehicle trajectories and evaluate the result using time varying shortest path solutions.
      Keywords
      Time Series
      Relevance Feedback, Diversity
      Autoencoder
      Sparsity
      Time-Varying Graphs
      Permalink
      http://hdl.handle.net/11693/47732
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      • Dept. of Computer Engineering - Ph.D. / Sc.D. 75
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