Term of Award
Master of Science in Mathematics (M.S.)
Document Type and Release Option
Thesis (open access)
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This work is licensed under a Creative Commons Attribution 4.0 License.
Department of Mathematical Sciences
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Finding Association Rules has been a popular unsupervised learning technique for dis covering interesting patterns in commercial data for well over two decades. The method seeks groups of data attributes and their values where their probability density of these attributesattherespectivevaluesismaximized. Therearecurrentlywell-establishedmeth ods for tackling this problem for data with categorical (discrete) attributes. However, for the cases of data with continuous variables, the techniques are largely focusing on cate gorizing continuous variables into intervals of interest and then relying on the categorical data methods to address the problem. We address the problem of ﬁnding association rules patterns in mixed data by using another unsupervised learning technique, clustering. The data attributes are organized into categorical and continuous attributes groups, and then we ﬁnd the association rules patterns among attributes in each group that would satisfy the re quired probability density thresholds. We have implemented and tested our method, which produces very good results when used on real, mixed data
Elson, Welendawa Acharige Charith A., "Association Rules Patterns Discovery From Mixed Data" (2020). Electronic Theses and Dissertations. 2183.
Research Data and Supplementary Material