Theses and dissertations

This collection includes both ASU Theses and Dissertations, submitted by graduate students, and the Barrett, Honors College theses submitted by undergraduate students. 

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Big Data Generator and Evaluation of a Similarity Grouping Operator

Description
As Big Data becomes more relevant, existing grouping and clustering algorithms will need to be evaluated for their effectiveness with large amounts of data. Previous work in Similarity Grouping proposes a possible alternative to existing data analytics tools, which acts as a hybrid between fast grouping and insightful clustering. We,…

As Big Data becomes more relevant, existing grouping and clustering algorithms will need to be evaluated for their effectiveness with large amounts of data. Previous work in Similarity Grouping proposes a possible alternative to existing data analytics tools, which acts as a hybrid between fast grouping and insightful clustering. We, the SimCloud Team, proposed Distributed Similarity Group-by (DSG), a distributed implementation of Similarity Group By. Experimental results show that DSG is effective at generating meaningful clusters and has a lower runtime than K-Means, a commonly used clustering algorithm. This document presents my personal contributions to this team effort. The contributions include the multi-dimensional synthetic data generator, execution of the Increasing Scale Factor experiment, and presentations at the NCURIE Symposium and the SISAP 2019 Conference.
ContributorsWallace, Xavier Guillermo (Author) / Silva, Yasin (Thesis director) / Kuai, Xu (Committee member) / School for the Future of Innovation in Society (Contributor) / School of Mathematical and Natural Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2019-12