Term of Award

Fall 2020

Degree Name

Master of Science in Mathematics (M.S.)

Document Type and Release Option

Thesis (open access)

Copyright Statement / License for Reuse

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


Department of Mathematical Sciences

Committee Chair

Ionut Iacob

Committee Member 1

Goran Lesaja

Committee Member 2

Hua Wang

Committee Member 3

Ionut Iacob

Committee Member 3 Email



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

OCLC Number


Research Data and Supplementary Material