Interactive Visual Data Exploration with Subjective Feedback

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

doi:10.1007/978-3-319-46227-1_14

Cited by 9 publications

(12 citation statements)

References 16 publications

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“…Indeed this appears to be the most relevant question to be addressed in the future. Finally, we aim to integrate this method with SIDE [30], [31], our online tool for exploration of numerical data, which currently does not use any labels or description attributes.…”

Section: Discussionmentioning

confidence: 99%

Subjectively Interesting Subgroup Discovery on Real-Valued Targets

Lijffijt

Kang

Duivesteijn

et al. 2018

2018 IEEE 34th International Conference on Data Engineering (ICDE)

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Deriving insights from high-dimensional data is one of the core problems in data mining. The difficulty mainly stems from the fact that there are exponentially many variable combinations to potentially consider, and there are infinitely many if we consider weighted combinations, even for linear combinations. Hence, an obvious question is whether we can automate the search for interesting patterns and visualizations. In this paper, we consider the setting where a user wants to learn as efficiently as possible about real-valued attributes. For example, to understand the distribution of crime rates in different geographic areas in terms of other (numerical, ordinal and/or categorical) variables that describe the areas. We introduce a method to find subgroups in the data that are maximally informative (in the formal Information Theoretic sense) with respect to a single or set of real-valued target attributes. The subgroup descriptions are in terms of a succinct set of arbitrarily-typed other attributes. The approach is based on the Subjective Interestingness framework FORSIED to enable the use of prior knowledge when finding most informative non-redundant patterns, and hence the method also supports iterative data mining.

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Section: Discussionmentioning

confidence: 99%

Subjectively Interesting Subgroup Discovery on Real-Valued Targets

Lijffijt

Kang

Duivesteijn

et al. 2018

2018 IEEE 34th International Conference on Data Engineering (ICDE)

Self Cite

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“…A branch of research focuses on visual analytics for spatio-temporal modeling (Zheng et al 2016). Interactive and human-guided methods are employed to discover the behavioral patterns and understand the heterogeneous information in the urban data (Puolamäki et al 2016;Chen et al 2018). The differences between our work and the work before are two-fold.…”

Section: Spatio-temporal Modelingmentioning

confidence: 99%

Exceptional spatio-temporal behavior mining through Bayesian non-parametric modeling

Pei

Duivesteijn

et al. 2020

Data Min Knowl Disc

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Collective social media provides a vast amount of geo-tagged social posts, which contain various records on spatio-temporal behavior. Modeling spatio-temporal behavior on collective social media is an important task for applications like tourism recommendation, location prediction and urban planning. Properly accomplishing this task requires a model that allows for diverse behavioral patterns on each of the three aspects: spatial location, time, and text. In this paper, we address the following question: how to find representative subgroups of social posts, for which the spatio-temporal behavioral patterns are substantially different from the behavioral patterns in the whole dataset? Selection and evaluation are the two challenging problems for finding the exceptional subgroups. To address these problems, we propose BNPM: a Bayesian non-parametric model, to model spatio-temporal behavior and infer the exceptionality of social posts in subgroups. By training BNPM on a large amount of randomly sampled subgroups, we can get the global distribution of behavioral patterns. For each given subgroup of social posts, its posterior distribution can be inferred by BNPM. By comparing the posterior distribution with the global distribution, we can quantify the exceptionality of each given subgroup. The exceptionality scores are used to guide the search process within the exceptional model mining framework to automatically discover the exceptional subgroups. Various experiments are conducted to evaluate the effectiveness and efficiency of our method. On four real-world datasets our method discovers subgroups coinciding with events, subgroups distinguishing professionals from tourists, and subgroups whose consistent exceptionality can only be truly appreciated by combining exceptional spatio-temporal and exceptional textual behavior.

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“…This work is motivated by the ideas in [14] in which a similar system was constructed using constrained randomization. The constrained randomization approach [15], [16] is similar to the Maximum Entropy distribution used here, but it relies on sampling of the data and no direct distribution assumptions are made.…”

Section: Related Workmentioning

confidence: 99%

Interactive visual data exploration with subjective feedback: an information-theoretic approach

Puolamäki

Oikarinen

Kang

et al. 2019

Data Min Knowl Disc

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Visual exploration of high-dimensional real-valued datasets is a fundamental task in exploratory data analysis (EDA). Existing methods use predefined criteria to choose the representation of data. There is a lack of methods that (i) elicit from the user what she has learned from the data and (ii) show patterns that she does not know yet. We construct a theoretical model where identified patterns can be input as knowledge to the system. The knowledge syntax here is intuitive, such as "this set of points forms a cluster", and requires no knowledge of maths. This background knowledge is used to find a Maximum Entropy distribution of the data, after which the system provides the user data projections in which the data and the Maximum Entropy distribution differ the most, hence showing the user aspects of the data that are maximally informative given the user's current knowledge. We provide an open source EDA system with tailored interactive visualizations to demonstrate these concepts. We study the performance of the system and present use cases on both synthetic and real data. We find that the model and the prototype system allow the user to learn information efficiently from various data sources and the system works sufficiently fast in practice. We conclude that the information theoretic approach to exploratory data analysis where patterns observed by a user are formalized as constraints provides a principled, intuitive, and efficient basis for constructing an EDA system.

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Interactive Visual Data Exploration with Subjective Feedback

Cited by 9 publications

References 16 publications

Subjectively Interesting Subgroup Discovery on Real-Valued Targets

Subjectively Interesting Subgroup Discovery on Real-Valued Targets

Exceptional spatio-temporal behavior mining through Bayesian non-parametric modeling

Interactive visual data exploration with subjective feedback: an information-theoretic approach

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