title: Constructing overview+detail dendrogram-matrix views.
abstract: A dendrogram that visualizes a clustering hierarchy is often integrated with a reorderable matrix for pattern identification. The method is widely used in many research fields including biology, geography, statistics, and data mining. However, most dendrograms do not scale up well, particularly with respect to problems of graphical and cognitive information overload. This research proposes a strategy that links an overview dendrogram and a detail-view dendrogram, each integrated with a reorderable matrix. The overview displays only a user-controlled, limited number of nodes that represent the ""skeleton" of a hierarchy. The detail view displays the sub-tree represented by a selected meta-node in the overview. The research presented here focuses on constructing a concise overview dendrogram and its coordination with a detail view. The proposed method has the following benefits: dramatic alleviation of information overload, enhanced scalability and data abstraction quality on the dendrogram, and the support of data exploration at arbitrary levels of detail. The contribution of the paper includes a new metric to measure the "importance" of nodes in a dendrogram; the method to construct the concise overview dendrogram from the dynamically-identified, important nodes; and measure for evaluating the data abstraction quality for dendrograms. We evaluate and compare the proposed method to some related existing methods, and demonstrating how the proposed method can help users find interesting patterns through a case study on county-level U.S. cervical cancer mortality and demographic data.
authors.affiliation: Department of Geography, Pennsylvania State University, PA, USA. jxc93@psu.edu
authors.forename: Jin
authors.initials: J
authors.lastname: Chen
authors.forename: Alan M
authors.initials: AM
authors.lastname: MacEachren
authors.forename: Donna J
authors.initials: DJ
authors.lastname: Peuquet
mesh_terms: D000465:Algorithms; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods*; D005260:Female; D006801:Humans; D010363:Pattern Recognition, Automated / Q000379:methods*; D002583:Uterine Cervical Neoplasms