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Issue No. 12 - Dec. (2012 vol. 18)
ISSN: 1077-2626
pp: 2382-2391
B. Rieck , Interdiscipl. Center for Sci. Comput. (IWR), Heidelberg Univ., Heidelberg, Germany
H. Mara , Interdiscipl. Center for Sci. Comput. (IWR), Heidelberg Univ., Heidelberg, Germany
H. Leitte , Interdiscipl. Center for Sci. Comput. (IWR), Heidelberg Univ., Heidelberg, Germany
The extraction of significant structures in arbitrary high-dimensional data sets is a challenging task. Moreover, classifying data points as noise in order to reduce a data set bears special relevance for many application domains. Standard methods such as clustering serve to reduce problem complexity by providing the user with classes of similar entities. However, they usually do not highlight relations between different entities and require a stopping criterion, e.g. the number of clusters to be detected. In this paper, we present a visualization pipeline based on recent advancements in algebraic topology. More precisely, we employ methods from persistent homology that enable topological data analysis on high-dimensional data sets. Our pipeline inherently copes with noisy data and data sets of arbitrary dimensions. It extracts central structures of a data set in a hierarchical manner by using a persistence-based filtering algorithm that is theoretically well-founded. We furthermore introduce persistence rings, a novel visualization technique for a class of topological features-the persistence intervals-of large data sets. Persistence rings provide a unique topological signature of a data set, which helps in recognizing similarities. In addition, we provide interactive visualization techniques that assist the user in evaluating the parameter space of our method in order to extract relevant structures. We describe and evaluate our analysis pipeline by means of two very distinct classes of data sets: First, a class of synthetic data sets containing topological objects is employed to highlight the interaction capabilities of our method. Second, in order to affirm the utility of our technique, we analyse a class of high-dimensional real-world data sets arising from current research in cultural heritage.
topology, data analysis, data structures, data visualisation, history, information filtering, pattern classification, cultural heritage, multivariate data analysis, topological signatures, significant structures extraction, arbitrary high-dimensional data sets, data points classification, problem complexity, stopping criterion, visualization pipeline, algebraic topology, persistent homology, topological data analysis, noisy data, central structures, hierarchical manner, persistence-based filtering algorithm, persistence rings, topological features, large data sets, interactive visualization techniques, parameter space evaluation, relevant structures, analysis pipeline, synthetic data sets, topological objects, interaction capability, high-dimensional real-world data sets, Network topology, Clustering methods, Multivariate data sets, clustering, Topological persistence, multivariate data

H. Leitte, H. Mara and B. Rieck, "Multivariate Data Analysis Using Persistence-Based Filtering and Topological Signatures," in IEEE Transactions on Visualization & Computer Graphics, vol. 18, no. , pp. 2382-2391, 2012.
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