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Author ORCID Identifier

https://orcid.org/0000-0003-2529-4036

AccessType

Open Access Dissertation

Document Type

dissertation

Degree Name

Doctor of Philosophy (PhD)

Degree Program

Computer Science

Year Degree Awarded

2021

Month Degree Awarded

September

First Advisor

Barna Saha

Second Advisor

Arya Mazumdar

Third Advisor

Gerome Miklau

Fourth Advisor

Divesh Srivastava

Subject Categories

Data Science

Abstract

A growing number of data-based applications are used for decision-making that have far-reaching consequences and significant societal impact. Entity resolution, community detection and taxonomy construction are some of the building blocks of these applications and for these methods, clustering is the fundamental underlying concept. Therefore, the use of accurate, robust and scalable methods for clustering cannot be overstated. We tackle the various facets of clustering with a multi-pronged approach described below.

1. While identification of clusters that refer to different entities is challenging for automated strategies, it is relatively easy for humans. We study the robustness of clustering methods that leverage supervision through an oracle i.e an abstraction of crowdsourcing. Additionally, we focus on scalability to handle web-scale datasets.

2. In community detection applications, a common setback in evaluation of the quality of clustering techniques is the lack of ground truth data. We propose a generative model that considers dependent edge formation and devise techniques for efficient cluster recovery.

DOI

https://doi.org/10.7275/24169035

Creative Commons License

Creative Commons Attribution-Share Alike 4.0 License
This work is licensed under a Creative Commons Attribution-Share Alike 4.0 License.

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Data Science Commons

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