A Tool for Subjective and Interactive Visual Data Exploration

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

doi:10.1007/978-3-319-46131-1_1

Cited by 5 publications

(7 citation statements)

References 10 publications

(7 reference statements)

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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)

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“…The case studies are completed with the a JavaScript version of our tool, which is available freely online, along with the used data for reproducibility. 1 [4] The full interface of SIDE is shown in Figure 3. SIDE was designed according to the three principles for 'visually controllable data mining' [3], which essentially state that both the model and the interactions should be transparent to users, and that the analysis method should be fast enough such that the user does not lose its trail of thought.…”

Section: Proof-of-concept Tool Sidementioning

confidence: 99%

“…In this paper we introduce a generically applicable method based on constrained randomizations for finding interesting projections of data, given some prior knowledge about that data. We present use cases of interactive visual exploration of high-dimensional data with the aid of a proof-of-concept tool [4] that demonstrates the presented framework. The method's aim is to aid users in Manuscript received xxxx; revised yyyy.…”

Section: Introductionmentioning

confidence: 99%

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A Constrained Randomization Approach to Interactive Visual Data Exploration with Subjective Feedback

Kang

Puolamäki

Lijffijt

et al. 2019

IEEE Trans. Knowl. Data Eng.

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Data visualization and iterative/interactive data mining are growing rapidly in attention, both in research as well as in industry. However, while there are plethora of advanced data mining methods and lots of works in the field of visualisation, integrated methods that combine advanced visualization and/or interaction with data mining techniques in a principled way are rare. We present a framework based on constrained randomization which lets users explore high-dimensional data via 'subjectively informative' two-dimensional data visualizations. The user is presented with 'interesting' projections, allowing users to express their observations using visual interactions that update a background model representing the user's belief state. This background model is then considered by a projection-finding algorithm employing data randomization to compute a new 'interesting' projection. By providing users with information that contrasts with the background model, we maximize the chance that the user encounters striking new information present in the data. This process can be iterated until the user runs out of time or until the difference between the randomized and the real data is insignificant. We present two case studies, one controlled study on synthetic data and another on census data, using the proof-of-concept tool SIDE that demonstrates the presented framework.

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“…Recently, an iterative data mining paradigm [1][2][3][4] has been presented and also realised in software [6][7][8] with the emphasis that the user wants to find patterns that are subjectively interesting given what she or he currently knows about the data. The system shows the user maximally informative views, i.e., views that contrast the most with the user's current knowledge.…”

Section: Introductionmentioning

confidence: 99%

Tiler: Software for Human-Guided Data Exploration

Henelius

Oikarinen

Puolamäki

2019

Machine Learning and Knowledge Discovery in Databases

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Understanding relations in datasets is important for the successful application of data mining and machine learning methods. This paper describes tiler, a software tool for interactive visual explorative data analysis realising the interactive Human-Guided Data Exploration framework. tiler allows a user to formulate different hypotheses concerning the relations in a dataset. Data samples corresponding to these hypotheses are then compared visually, allowing the user to gain insight into relations in the dataset. The exploration process is iterative and the user gradually builds up his or her understanding of the data.

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A Tool for Subjective and Interactive Visual Data Exploration

Cited by 5 publications

References 10 publications

Subjectively Interesting Subgroup Discovery on Real-Valued Targets

Subjectively Interesting Subgroup Discovery on Real-Valued Targets

A Constrained Randomization Approach to Interactive Visual Data Exploration with Subjective Feedback

Tiler: Software for Human-Guided Data Exploration

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