Sequential Clustering via Recurrence Network Analysis

Zhang, Zhang (2018) Sequential Clustering via Recurrence Network Analysis. Doctoral Dissertation, University of Pittsburgh. (Unpublished)

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Abstract

In this dissertation, we propose a new sequential clustering method based on nonlinear dynamic theories and exponential random graph models (ERGMs), a class of social network models. In particular, we convert each sequence to its recurrence network and make use of the flexibility and information of the network. Our algorithm shows good performance on simulated data, real-world data and public benchmark data based on clustering evaluation metrics.

To make sure the connection between a sequence and a network is reliable, we also conduct a study to examine whether the network contains enough information of the original sequence. We consider recurrence networks as images and build state of the art convolutional neural network (CNN) models based on image data to predict the labels of original sequences. Our method is very competitive compared with other advanced sequential classification methods. This also verifies that recurrence networks are very informative and give us enough information for real-world applications.

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Details

Item Type:	University of Pittsburgh ETD
Status:	Unpublished
Creators/Authors:	Creators Email Pitt Username ORCID Zhang, Zhang stat.dzhang@gmail.com zhz46
ETD Committee:	Title Member Email Address Pitt Username ORCID Committee Chair Iyengar, Satish ssi@pitt.edu ssi Committee Member Van Panhuis, Wilbert wilbert.van.panhuis@pitt.edu wilbert.van.panhuis Committee Member Cheng, Yu yucheng@pitt.edu yucheng Committee Member Chen, Kehui khchen@pitt.edu khchen
Date:	31 January 2018
Date Type:	Publication
Defense Date:	6 December 2017
Approval Date:	31 January 2018
Submission Date:	7 December 2017
Access Restriction:	1 year -- Restrict access to University of Pittsburgh for a period of 1 year.
Number of Pages:	68
Institution:	University of Pittsburgh
Schools and Programs:	Dietrich School of Arts and Sciences > Statistics
Degree:	PhD - Doctor of Philosophy
Thesis Type:	Doctoral Dissertation
Refereed:	Yes
Uncontrolled Keywords:	Sequential clustering; Nonliear dynamical systems; Recurrence networks; Exponential random graph models; Recurrence image classification; Convolutional neural networks
Date Deposited:	31 Jan 2018 19:33
Last Modified:	31 Jan 2019 06:15
URI:	http://d-scholarship.pitt.edu/id/eprint/33589

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