Mingyao Li, PhD, Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania.
Peter Mueller, PhD, Department of Mathematics, Department of Statistics and Data Sciences, University of Texas at Austin
Lu Mao, PhD, Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison
Snehalata Huzurbazar, PhD, Department of Biostatistics, West Virginia University
Meetings of the Eastern North American Region of the International Biometric Society (a.k.a. "ENAR meetings") are held in late March or early April each year and reflect the broad interests of the Society, including both quantitative techniques and application areas. Faculty and student presenters from the Department of Biostatistics regularly participate giving invited talks, contributed talks, and poster presentations.
The Joint Statistical Meetings, known simply as "JSM", is the largest gathering of statisticians held annually in North American. Faculty and student presenters from the Department of Biostatistics regularly participate giving invited talks, contributed talks, and poster presentations. Our students often receive top awards and participate in the affiliated career marketplace at the event.
Abraham Apfel of the Department of Biostatistics defends his dissertation on "A Stability Analysis of Sparse K-means"
Graduate faculty of the University and all other interested parties are invited to attend.
Sparse K-Means clustering is an established method of simultaneously excluding uninformative features and clustering the observations. This is particularly useful in a high dimensional setting such as micro-array. However, the subsets of features selected is often inaccurate when there are overlapping clusters, which adversely affects the clustering results. The current method also tends to be inconsistent, yielding high variability in the number of features selected.
We propose to combine a stability analysis with Sparse K-Means via performing Sparse K-Means on subsamples of the original data to yield accurate and consistent feature selection. After reducing the dimensions to an accurate, small subset of features, the standard K-Means clustering procedure is performed to yield accurate clustering results. Our method demonstrates improvement in accuracy and reduction in variability providing consistent feature selection as well as a reduction in the clustering error rate (CER) from the previously established Sparse K-Means clustering methodology. Our method continues to perform well in situations with strong cluster overlap where the previous methods were unsuccessful.
Public health significance: Clustering analysis on transcriptomic data has shown success in disease phenotyping and subgroup discovery. However, with current methodology, there is a lack of confidence in terms of the accuracy and reliability of the results, as they can be highly variable. With our methodology, we hope to allow the researcher to use cluster analysis to achieve disease phenotyping and subgroup discovery with confidence that they are uncovering accurate and stable results.
Last Updated On Monday, October 23, 2017 by Valenti, Renee Nerozzi
Created On Tuesday, April 04, 2017