2026-05-26T00:34:57Zhttps://keep.lib.asu.edu/oai/requestoai:keep.lib.asu.edu:node-1321042024-12-18T20:58:30Zoai_pmh:alloai_pmh:repo_items132104
https://hdl.handle.net/2286/R.I.54737
http://rightsstatements.org/vocab/InC/1.0/
2019-12
14 pages
eng
Wallace, Xavier Guillermo
Silva, Yasin
Kuai, Xu
School for the Future of Innovation in Society
School of Mathematical and Natural Sciences
Barrett, The Honors College
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.
Big Data
Grouping
Simularity
Computer Science
Big Data Generator and Evaluation of a Similarity Grouping Operator