Due to the COVID-19 Pandemic, the University has instruction to cancel all in-person events through the Spring semester to adhere to city and state orders limiting public gatherings. Events still running must now take place Online— listed events will include a link in which one may access the Online webspace:

To view Ms. Beumer’s defense, enter her Online chatroom via Microsoft Team’s— it will open at 4:00 pm on Friday, April 24, 2020.

Evaluation of Text Document Clustering using k-Means

Ms. Lisa Beumer
University of Wisconsin-Milwaukee
MS Graduate Student – Teaching Assistant

“Due to the development in technology and digitization the amount of data is rapidly increasing. So, it is increasingly difficult for a user to find relevant information to his or her needs. In order to filter and extract  knowledge from digital texts, text mining algorithms are used. One fundamental text mining task is text document clustering. The goal of this unsupervised learning problem is to group documents into categories clusters) based on their similarity than texts within clusters are more similar that texts between different clusters. The clustering process consist of multiple steps: Collect data, prepare data using text pre-processing techniques, extract features, choose a clustering algorithm (here k-Means) and a similarity measure, generate clustering result, evaluate and interpret it. Since all steps effect the clustering result, the methods used in each step are compared in order to find potential ineffectiveness. The evaluation is based on the following data sets: 20 newsgroups, Jeopardy! questions and Reddit comments.”

Committee Members:
Profs. Istvan Lauko (Advisor); Gabriella Pinter & Vincent Larson

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