Guiding the Exploration of Scatter Plot Data Using Motif-Based Interest Measures

Lin Shao, Timo Schleicher, Michael Behrisch, Tobias Schreck, Ivan Sipiran, Daniel A. Keim

Producción científica: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

8 Citas (Scopus)


Finding interesting patterns in large scatter plot spaces is a challenging problem and becomes even more difficult with increasing number of dimensions. Previous approaches for exploring large scatter plot spaces like e.g., the well-known Scagnostics approach, mainly focus on ranking scatter plots based on their global properties. However, often local patterns contribute significantly to the interestingness of a scatter plot. We are proposing a novel approach for the automatic determination of interesting views in scatter plot spaces based on analysis of local scatter plot segments. Specifically, we automatically classify similar local scatter plot segments, which we call scatter plot motifs. Inspired by the well-known tf-idf approach from information retrieval, we compute local and global quality measures based on certain frequency properties of the local motifs. We show how we can use these to filter, rank and compare scatter plots and their incorporated motifs. We demonstrate the usefulness of our approach with synthetic and real-world data sets and showcase our corresponding data exploration tool that visualizes the distribution of local scatter plot motifs in relation to a large overall scatter plot space.

Idioma originalInglés
Título de la publicación alojada2015 Big Data Visual Analytics, BDVA 2015
EditoresUlrich Engelke, Tomasz Bednarz, Julian Heinrich, Karsten Klein, Quang Vinh Nguyen
EditorialInstitute of Electrical and Electronics Engineers Inc.
ISBN (versión digital)9781467373432
EstadoPublicada - 30 oct. 2015
EventoBig Data Visual Analytics, BDVA 2015 - Hobart, Australia
Duración: 22 set. 201525 set. 2015

Serie de la publicación

Nombre2015 Big Data Visual Analytics, BDVA 2015


ConferenciaBig Data Visual Analytics, BDVA 2015


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