Improving cluster analysis with automatic variable selection based on trees
Orr, Anton D.
Buttrey, Samuel E.
Whitaker, Lyn R.
MetadataShow full item record
Clustering is an algorithmic technique that aims to group similar objects together in order to give users better understanding of the underlying structure of their data. It can be thought of as a two-step process. The first step is to measure the distances among the objects to determine how dissimilar they are. The second, clustering, step takes the dissimilarity measurements and assigns each object to a cluster. We examine three distance measures proposed by Buttrey at the Joint Statistical Meeting in Seattle, August 2006 based on classification and regression trees to address problems with determining dissimilarity. Current algorithms do not simultaneously address the issues of automatic variable selection, independence from variable scaling, resistance to monotonic transformation and datasets of mixed variable types. These "tree distances" are compared with an existing dissimilarity algorithm and two newer methods using four well-known datasets. These datasets contain numeric, categorical and mixed variable types. In addition, noise variables are added to test the ability of each algorithm to automatically select important variables. The tree distances offer much improvement for the problems they aimed to address, performing well against competitors amongst numerical datasets, and outperforming in the cases of categorical and mixed variable type datasets.
Approved for public release; distribution is unlimited
Showing items related by title, author, creator and subject.
McArdle, Ryan P. (Monterey, California: Naval Postgraduate School, 2017-12);Over the past decade, a deluge of large and complex datasets (aka big data) has overwhelmed the scientific community. Traditional computing architectures were not capable of processing the data efficiently, or in some ...
Lynch, Sarah K. (Monterey, California: Naval Postgraduate School, 2014-03);Clustering is the process of putting observations into groups based on their distance, or dissimilarity, from one another. Measuring distance for continuous variables often requires scaling or monotonic transformation. ...
Kruse, Fred A.; Olsen, R.C. (SPIE, 2010);Urban areas are highly variable in remote sensing data, thus it can be difficult to detect changes over time caused by development or by movement of specific targets. This research is a first attempt at exploration of ...