Degree Name

MS (Master of Science)

Program

Mathematical Sciences

Date of Award

8-2018

Committee Chair or Co-Chairs

JeanMarie Hendrickson

Committee Members

Robert M Price Jr., Nicole Lewis

Abstract

Sorting out data into partitions is increasing becoming complex as the constituents of data is growing outward everyday. Mixed data comprises continuous, categorical, directional functional and other types of variables. Clustering mixed data is based on special dissimilarities of the variables. Some data types may influence the clustering solution. Assigning appropriate weight to the functional data may improve the performance of the clustering algorithm. In this paper we use the extension of the Gower coefficient with judciously chosen weight for the L2 to cluster mixed data.The benefits of weighting are demonstrated both in in applications to the Buoy data set as well simulation studies. Our studies show that clustering algorithms with application of proper weight give superior recovery level when a set of data with mixed continuous, categorical directional and functional attributes is clustered. We discuss open problems for future research in clustering mixed data.

Document Type

Thesis - Open Access

Copyright

Copyright by the authors.

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