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Visualization of Clustering Solutions for Large Multi-dimensional Sequential Datasets

Dornala, Maninder
http://orcid.org/0000-0001-5696-7489
http://rave.ohiolink.edu/etdc/view?acc_num=ysu1525869411092807

Abstract Details

2018, Master of Computing and Information Systems, Youngstown State University, Department of Computer Science and Information Systems.
The research presented is focused on the development of algorithms targeted towards analyzing data in the form of categorical time series or sequences. The wide availability of mobile devices and sensors connected to the Internet, makes it easier to collect datasets to model long-term user behavior. Nevertheless, performing fundamental analytical operations, such as clustering for grouping these data based on similarity patterns, has proved challenging due to the categorical nature of the data, the multiple variables to consider and their corruption by missing values. The classical metric type similarity distances have to be replaced with "edit" type distances, such as Optimal Matching. We developed this approach with the aim of studying the effect of similarity measure choice on clustering and dimensionality reduction methods applied to long-term life cycle trajectories. The discovered patterns can help providing better decision making and public policy design.
Alina Lazar, PhD (Advisor)
Bonita Sharif, PhD (Committee Member)
John Sullins, PhD (Committee Member)
90 p.
Computer Science; Information Systems; Information Technology
Optimal Matching; hierarchical and partitioning algorithms; development of algorithms targeted towards clustering applied to data in the form of categorical time series or sequences

Recommended Citations

Citations

  • Dornala, M. (2018). Visualization of Clustering Solutions for Large Multi-dimensional Sequential Datasets [Master's thesis, Youngstown State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=ysu1525869411092807

    APA Style (7th edition)

  • Dornala, Maninder. Visualization of Clustering Solutions for Large Multi-dimensional Sequential Datasets. 2018. Youngstown State University, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=ysu1525869411092807.

    MLA Style (8th edition)

  • Dornala, Maninder. "Visualization of Clustering Solutions for Large Multi-dimensional Sequential Datasets." Master's thesis, Youngstown State University, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=ysu1525869411092807

    Chicago Manual of Style (17th edition)

ysu1525869411092807
384
© 2018, all rights reserved.
This open access ETD is published by Youngstown State University and OhioLINK.