Deng, Junning Lijffijt, Jefrey Kang, Bo De Bie, Tijl

This paper introduces an approach to find motifs in time series that are \emph{subjectively interesting}. That is, the aim is to find motifs that are surprising given an informative background distribution, which may for example correspond to the prior knowledge of a user of the tool. We quantify this surprisal using information theory, and more pa...

Deng, Junning Lijffijt, Jefrey Kang, Bo De Bie, Tijl

This paper introduces an approach to find motifs in time series that are \emph{subjectively interesting}. That is, the aim is to find motifs that are surprising given an informative background distribution, which may for example correspond to the prior knowledge of a user of the tool. We quantify this surprisal using information theory, and more pa...

Deng, Junning Lijffijt, Jefrey Kang, Bo De Bie, Tijl

This paper introduces an approach to find motifs in time series that are \emph{subjectively interesting}. That is, the aim is to find motifs that are surprising given an informative background distribution, which may for example correspond to the prior knowledge of a user of the tool. We quantify this surprisal using information theory, and more pa...

Deng, Junning Lijffijt, Jefrey Kang, Bo De Bie, Tijl

This paper introduces an approach to find motifs in time series that are \emph{subjectively interesting}. That is, the aim is to find motifs that are surprising given an informative background distribution, which may for example correspond to the prior knowledge of a user of the tool. We quantify this surprisal using information theory, and more pa...

Kang, Bo Puolamäki, Kai Lijffijt, Jefrey De Bie, Tijl

We present SIDE, a tool for Subjective and Interactive Visual Data Exploration, which lets users explore high dimensional data via subjectively informative 2D data visualizations. Many existing visual analytics tools are either restricted to specific problems and domains or they aim to find visualizations that align with user’s belief about the dat...

Kang, Bo Puolamäki, Kai Lijffijt, Jefrey De Bie, Tijl

We present SIDE, a tool for Subjective and Interactive Visual Data Exploration, which lets users explore high dimensional data via subjectively informative 2D data visualizations. Many existing visual analytics tools are either restricted to specific problems and domains or they aim to find visualizations that align with user’s belief about the dat...

Kang, Bo Lijffijt, Jefrey Santos-Rodríguez, Raúl De Bie, Tijl

Methods that find insightful low-dimensional projections are essential to effectively explore high-dimensional data. Principal Component Analysis is used pervasively to find low-dimensional projections, not only because it is straightforward to use, but it is also often effective, because the variance in data is often dominated by relevant structur...

Puolamäki, Kai Kang, Bo Lijffijt, Jefrey De Bie, Tijl

Data visualization and iterative/interactive data mining are growing rapidly in attention, both in research as well as in industry. However, integrated methods and tools that combine advanced visualization and data mining techniques are rare, and those that exist are often specialized to a single problem or domain. In this paper, we introduce a nov...

Kang, Bo Lijffijt, Jefrey Santos-Rodríguez, Raúl De Bie, Tijl

Methods that find insightful low-dimensional projections are essential to effectively explore high-dimensional data. Principal Component Analysis is used pervasively to find low-dimensional projections, not only because it is straightforward to use, but it is also often effective, because the variance in data is often dominated by relevant structur...

Kang, Bo Lijffijt, Jefrey Santos-Rodríguez, Raúl De Bie, Tijl

Methods that find insightful low-dimensional projections are essential to effectively explore high-dimensional data. Principal Component Analysis is used pervasively to find low-dimensional projections, not only because it is straightforward to use, but it is also often effective, because the variance in data is often dominated by relevant structur...