Relevance feedback and sparsity handling methods for temporal data
Author(s)
Advisor
Date
2018-08Publisher
Bilkent University
Language
English
Type
ThesisItem Usage Stats
133
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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.