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 data. In contrast, our generic tool computes data visualizations that are surprising given a user’s current understanding of the data. The user’s belief state is represented as a set of projection tiles. Hence, this user-awareness offers users an efficient way to interactively explore yet-unknown features of complex high dimensional datasets

C Ware

D Paurat

DJ Hand

J Lijffijt

T Bie De

T Ruotsalo

V Dzyuba

English

Bo Kang

Kai Puolamäki

Jefrey Lijffijt

Tijl De Bie

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A Tool for Subjective and Interactive Visual Data Exploration

Kang, Bo

Puolamäki, Kai

Lijffijt, Jefrey

De Bie, Tijl

Ghent University Academic Bibliography

This	manuscript	is	a	post-print	copy	of	the	following	article		Title:	A	Tool	for	Subjective	and	Interactive	Visual	Data	Exploration		Authors:	Bo	Kang	;	Kai	Puolamäki	;	Jefrey	Lijffijt	;	Tijl	De	Bie		The	final	publication	is	available	at	Springer	via	http://dx.doi.org/10.1007%2F978-3-319-46131-1_1	A Tool for Subjective and InteractiveVisual Data ExplorationBo Kang1, Kai Puolama¨ki2, Jefrey Lij jt1, Tijl De Bie11 Data Science Lab, Ghent University, Belgiumfirstname.lastname@ugent.be2 Finnish Institute of Occupational Health, Finlandkai.puolamaki@ttl.fiAbstract. We present SIDE, a tool for Subjective and Interactive Vi-sual Data Exploration, which lets users explore high dimensional data viasubjectively informative 2D data visualizations. Many existing visual an-alytics tools are either restricted to specific problems and domains or theyaim to find visualizations that align with user’s belief about the data. Incontrast, our generic tool computes data visualizations that are surpris-ing given a user’s current understanding of the data. The user’s beliefstate is represented as a set of projection tiles. Hence, this user-awarenesso↵ers users an e cient way to interactively explore yet-unknown featuresof complex high dimensional datasets.1 IntroductionExploratory Data Mining is the process of using data mining methods to gainnovel insights into data without having a specific goal in mind. To convey largeamounts of complex information, it is a logical choice to present this informationvisually, as the information bandwidth of the eye is much larger than the othersenses, and humans excel at spotting visual patterns [11]. Surprisingly, visualinteractive data mining tools are still rare.The few tools that exist are either designed for specific problems and domains(e.g., itemset and subgroup discovery [1,4,7], information retrieval [10], or analy-sis of networks [2]) and/or aim to present information that align with the user’sbeliefs (e.g., semi-supervised PCA [7]). However, users are typically interestedin finding structures in the data that contrast with their current knowledge [5].In this paper, we present a generic tool3 that enables users to e ciently ex-plore data via a sequence of 2D scatter plots, i.e., projections. It models the user’sbeliefs about data by iteratively incorporating their feedback, which in turn isutilized for calculating an updated data projection. SIDE operates iteratively,with three steps in each iteration (see Fig. 1). In step 1, it presents a user with a‘surprising’ data projection. In step 2, the user provides feedback about the pro-jection. Finally, in step 3, the background model is updated to reflect the user’s3 Our tool, SIDE, is freely accessible at http://www.interesting-patterns.net/forsied/a-tool-for-subjective-and-interactive-visual-data-explorationcurrent belief state. It then repeats from step 1, and shows a data projectionthat takes into account the updated background model.2 Subjectively Interesting ProjectionsSIDE employs a generic method for interactive visual exploration of high di-mensional data, with awareness of a user’s belief sate about the data. Due tospace constraints we limit ourselves to describe only the intuition and overviewof the approach. For a full description, we refer the reader to our paper [9].(1) Data Visualization(2) UserFeedback(3) UpdateBackgroundModelUserAlgorithmFig. 1: This three-step cycle illus-trates our tool’s flow of action.In order to present the user withsubjectively informative data projections,there are two modeling problems [3].First, we have to maintain a backgroundmodel throughout the exploration process.This model accumulates the user’s feed-back, which represents the knowledge theylearned from the data projections. Hence,this model represents a user’s current beliefabout the data.The second obstacle is quantification ofthe informativeness, for which we employconstrained randomization [6]. The idea is that we sample random data fromthe user’s current belief state, where the beliefs are modeled as constraints tothe randomization procedure. Then, we search for projections that contrast withthe random data, and hence that contrast with the current beliefs. That is, weassume that a data projection that (maximally) deviates from the beliefs willreveal subjectively novel structures.Then, an optimization problem arises to find a projection that makes thereal data maximally di↵erent from the randomized data. Currently the tool em-ploys the L1 distance, which can be optimized well using standard optimizationtoolboxes. We have not studied the choice of measure extensively yet.3 User InterfaceSIDE was designed according to three principles for visually controllable datamining [8], which essentially says that the model and the interactions should betransparent to users, and the analysis method should be fast enough such thatthe user does not lose their trail of thought. Figure 2 shows the user interfaceof our tool. The main component of this interface is the interactive scatter plot(Fig. 2a). The scatter plot visualizes the projected data (filled dots) and therandomized data (gray circles) using the same projection. By drawing circles(Fig. 2b), the user can highlight a projection tile pattern (i.e., a set of filleddots). Once a set of points is marked, the user can press either feedback button(Fig. 2c), indicating these points form a cluster. If the users believe the pointsare clustered only in the shown projection, they click ‘2D Constraint’, whileFig. 2: Visual layout of interactive dimensionality tool, which contains interactarea (a), projection meta information area (g), and snapshots area (h).‘Cluster Constraint’ indicates they are aware of the fact that these points willbe clustered in other dimensions as well. To identify the defined clusters, datapoints associated with the same feedback (i.e., user’s belief) are filled by thesame color (Fig. 2d), and their statistics are shown in a table. The user candefine multiple clusters in a single projection, and they can also undo (Fig. 2e)the feedback. Once a user finishes exploring the current projection, they can press‘Update Background Model’ (Fig. 2f). Then, the background model is updatedwith the provided feedback and a new scatter plot is computed and presentedto the user, etc.A few extra features are provided to assist the data exploration process: togain an intuitive understanding of a projection, the weight vectors associatedwith the projection axes are plotted as bar charts (Fig. 2g). At the bottom ofFigure 2g, a table lists the mean vectors of each colored point set (i.e., cluster).The exploration history is maintained by taking snapshots of the backgroundmodel when updated, together with the associated data projection (scatter plot)and bar charts (weight vectors). This history in reverse chronological order isillustrated in Figure 2h. The tool also allows a user to click and revert (Fig. 2i)back to a certain snapshot, to restart from that time point. This allows the user todiscover di↵erent aspects of a dataset more consistently. Finally, custom datasetscan be selected for analysis from the drop-down menu (Fig. 2j). Currently ourtool only works with CSV files and it also automatically sub-samples any data setso that the interactive experience is not compromised. By default, two datasetsare preloaded so that users can get familiar with the tool.4 ConclusionsWe presented SIDE, an interactive exploratory data mining tool that allowsusers to visually explore data. By modeling a user’s belief state, our tool isable to present users with views of data that contrast with and add to theircurrent knowledge. In contrast to the existing visual analytics systems, our toolis automatically tailored towards each specific user and able to cope with genericmining tasks. Thus, users can easily obtain new knowledge about data on topof their increasingly accurate understandings, providing a more e cient way ofnavigating the complex information space hidden in high-dimensional data.Acknowledgments This work was supported by the European Union throughthe ERC Consolidator Grant FORSIED (project reference 615517), Academy ofFinland (decision 288814), and Tekes (Revolution of Knowledge Work project).References1. Boley, M., Mampaey, M., Kang, B., Tokmakov, P., Wrobel, S.: One click mining:Interactive local pattern discovery through implicit preference and performancelearning. In: Proc. of KDD. pp. 27–35 (2013)2. Chau, D.H., Kittur, A., Hong, J.I., Faloutsos, C.: Apolo: making sense of largenetwork data by combining rich user interaction and machine learning. In: Proc.of CHI. pp. 167–176 (2011)3. De Bie, T.: Subjective interestingness in exploratory data mining. In: Proc. of IDA.pp. 19–31 (2013)4. Dzyuba, V., van Leeuwen, M.: Interactive discovery of interesting subgroup sets.In: Proc. of IDA. pp. 150–161 (2013)5. Hand, D.J., Mannila, H., Smyth, P.: Principles of Data Mining. MIT Press (2001)6. Lij jt, J., Papapetrou, P., Puolama¨ki, K.: A statistical significance testing ap-proach to mining the most informative set of patterns. DMKD 28(1), 238–263(2014)7. Paurat, D., Ga¨rtner, T.: Invis: A tool for interactive visual data analysis. In: Proc.of ECML–PKDD. pp. 672–676 (2013)8. Puolama¨ki, K., Papapetrou, P., Lij jt, J.: Visually controllable data mining meth-ods. In: Proc. of ICDMW. pp. 409–417 (2010)9. Puolama¨ki, K., Kang, B., Lij jt, J., De Bie, T.: Interactive visual data explorationwith subjective feedback. In: Proc. of ECML–PKDD (to appear) (2016)10. Ruotsalo, T., Jacucci, G., Myllyma¨ki, P., Kaski, S.: Interactive intent modeling:Information discovery beyond search. CACM 58(1), 86–92 (2015)11. Ware, C.: Information Visualization: Perception for Design. 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A tool for subjective and interactive visual data exploration

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A tool for subjective and interactive visual data exploration

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Ghent University Academic Bibliography