. Overview of a SOMFlow clustering graph that was created during our expert study to analyze speech intonation: First, a gender effect is identified (A) and removed using a domain-specific semitone normalization (B). The analyst created more detailed SOMs for artificial cells and added manual annotations (C) to filter noise caused by measurement errors. The resulting SOM reveals a relation to the pitch meta-attribute (D) and further data partitions allow the analyst to compare pitch contours of different speaker groups. Abstract-Clustering is a core building block for data analysis, aiming to extract otherwise hidden structures and relations from raw datasets, such as particular groups that can be effectively related, compared, and interpreted. A plethora of visual-interactive cluster analysis techniques has been proposed to date, however, arriving at useful clusterings often requires several rounds of user interactions to fine-tune the data preprocessing and algorithms. We present a multi-stage Visual Analytics (VA) approach for iterative cluster refinement together with an implementation (SOMFlow) that uses Self-Organizing Maps (SOM) to analyze time series data. It supports exploration by offering the analyst a visual platform to analyze intermediate results, adapt the underlying computations, iteratively partition the data, and to reflect previous analytical activities. The history of previous decisions is explicitly visualized within a flow graph, allowing to compare earlier cluster refinements and to explore relations. We further leverage quality and interestingness measures to guide the analyst in the discovery of useful patterns, relations, and data partitions. We conducted two pair analytics experiments together with a subject matter expert in speech intonation research to demonstrate that the approach is effective for interactive data analysis, supporting enhanced understanding of clustering results as well as the interactive process itself.
SOMFlow REF can iteratively facet and refine clusters, with the history of previous decisions visualized in a flow graph.
SOMFlow: Guided Exploratory Cluster Analysis with Self-Organizing Maps and Analytic Provenance
IEEE Transactions on Visualization and Computer Graphics
IEEE Transactions on Visualization and Computer Graphics
Computer Science Medicine