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Interactive Visual Exploration of Local Patterns in Large Scatterplot Spaces
Author(s) -
Chegini Mohammad,
Shao Lin,
Gregor Robert,
Lehmann Dirk J.,
Andrews Keith,
Schreck Tobias
Publication year - 2018
Publication title -
computer graphics forum
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.578
H-Index - 120
eISSN - 1467-8659
pISSN - 0167-7055
DOI - 10.1111/cgf.13404
Subject(s) - computer science , data mining , relevance (law) , visualization , similarity (geometry) , multivariate statistics , rank (graph theory) , artificial intelligence , pattern recognition (psychology) , information retrieval , machine learning , mathematics , image (mathematics) , combinatorics , political science , law
Analysts 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.