title: Clustering Scatter Plots Using Data Depth Measures.
abstract: Clustering is rapidly becoming a powerful data mining technique, and has been broadly applied to many domains such as bioinformatics and text mining. However, the existing methods can only deal with a data matrix of scalars. In this paper, we introduce a hierarchical clustering procedure that can handle a data matrix of scatter plots. To more accurately reflect the nature of data, we introduce a dissimilarity statistic based on "data depth" to measure the discrepancy between two bivariate distributions without oversimplifying the nature of the underlying pattern. We then combine hypothesis testing with hierarchical clustering to simultaneously cluster the rows and columns of the data matrix of scatter plots. We also propose novel painting metrics and construct heat maps to allow visualization of the clusters. We demonstrate the utility and power of our new clustering method through simulation studies and application to a microbe-host-interaction study.
authors.affiliation: Department of Statistics, University of California, Riverside, CA, USA.
authors.forename: Zhanpan
authors.initials: Z
authors.lastname: Zhang
authors.affiliation: Department of Statistics, University of California, Riverside, CA, USA.
authors.forename: Xinping
authors.initials: X
authors.lastname: Cui
authors.affiliation: Department of Statistics, University of California, Riverside, CA, USA.
authors.forename: Daniel R
authors.initials: DR
authors.lastname: Jeske
authors.affiliation: Department of Molecular and Medical Pharmacology, University of California, Los Angeles, CA, USA.
authors.forename: Xiaoxiao
authors.initials: X
authors.lastname: Li
authors.affiliation: Department of Molecular and Medical Pharmacology, University of California, Los Angeles, CA, USA ; Department of Pathology and Laboratory Medicine, University of California, Los Angeles, CA, USA.
authors.forename: Jonathan
authors.initials: J
authors.lastname: Braun
authors.affiliation: Department of Plant Pathology and Microbiology, University of California, Riverside, CA, USA.
authors.forename: James
authors.initials: J
authors.lastname: Borneman
keywords: Clustering; Data Depth; Quality Index; Scatter Plot; Visualization