This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. The goal of this project is to design new analytical and visual tools and techniques to provide insights into the record level information of multiple clustering algorithm results. Our work is going to address significant questions in the biomedical field by devising tools that enable scientists to look through multiple clustering methods and analyze the results, in order to aid the data exploration. The biomedical field has undergone strong growth over the past decade, generating massive data sets that require new approaches. A new research paradigm is created in this area, and the fields' approaches differ from the established fields of science that have recognized research procedures. Using visual techniques in order to harness the experimental scientist's intuition seems common sense but the strong dependence on automatic computation still pervades the field. Intellectual risk-taking is part of this undertaking, since practicing individuals in participating disciplines would otherwise recourse to interact with one another, attempt to speak a new common language, and make breakthroughs in a field so distant from their own. This research will accomplish three primary goals:
Specific Aim 1. Develop, implement and validate analytical measures of similarity of multiple clustering results (Year 1 and Year 2): (a) Formally define the similarity and dissimilarity of clustering results (b) Create novel analytical measures that capture the definitions of similarity and dissimilarity (c) Define and implement specific applications of the analytical measures;
Specific Aim 2. Construct novel visualizations that aid the identification of similar and dissimilar records, clusters and subsets (Year 3 and beyond) (a) Design and implement novel visualization techniques that work with the new analytical measures (b) Create and implement interaction methods that facilitate the exploration;
Specific Aim 3. Investigate and improve the performance of the techniques and algorithms and apply and extend the techniques and tools on large experimental data sets (Year 5).
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