Chegini, MohammadShao, LinGregor, RobertLehmann, Dirk JoachimAndrews, KeithSchreck, TobiasJeffrey Heer and Heike Leitte and Timo Ropinski2018-06-022018-06-0220181467-8659https://doi.org/10.1111/cgf.13404https://diglib.eg.org:443/handle/10.1111/cgf13404Analysts often use visualisation techniques like a scatterplot matrix (SPLOM) to explore multivariate datasets. The scatterplots of a SPLOM can help to identify and compare two-dimensional global patterns. However, local patterns which might only exist within subsets of records are typically much harder to identify and may go unnoticed among larger sets of plots in a SPLOM. This paper explores the notion of local patterns and presents a novel approach to visually select, search for, and compare local patterns in a multivariate dataset. Model-based and shape-based pattern descriptors are used to automatically compare local regions in scatterplots to assist in the discovery of similar local patterns. Mechanisms are provided to assess the level of similarity between local patterns and to rank similar patterns effectively. Moreover, a relevance feedback module is used to suggest potentially relevant local patterns to the user. The approach has been implemented in an interactive tool and demonstrated with two real-world datasets and use cases. It supports the discovery of potentially useful information such as clusters, functional dependencies between variables, and statistical relationships in subsets of data records and dimensions.[Humancentered computing]VisualizationVisualization systems and tools10.1111/cgf.1340499-109