Identifying Locally Interesting Motifs for Exploration of Scatter Plot Matrices

Scatter plots are effective diagrams to visualize distributions, clusters and correlations in two-dimensional data space. For highdimensional data, scatter plot matrices can be formed to show all two-dimensional combinations of dimensions. Several previous approaches for exploration of large scatter plot spaces have focused on ranking and sorting scatter plot matrices based on global patterns. However, often local patterns are of interest for scatter plot exploration. We present a preliminary idea to explore the scatter plot space by identifying significant local patterns (also called motifs in this work). Based on certain clustering algorithms and image-based descriptors, we identify and group a set of similar local candidate motifs in a large scatter plot space.