Data Exploration for Bisociative Knowledge Discovery: A Brief Overview of Tools and Evaluation Methods

Gossen, Tatiana; Nitsche, Marcus; Haun, Stefan; Nürnberger, Andreas

doi:10.1007/978-3-642-31830-6_20

Cited by 9 publications

(3 citation statements)

References 21 publications

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“…This focus on "finding the unexpected" obviously also requires rather different approaches to the creation, analysis and exploration of the underlying structure. Overviews of these three aspects can be found in [5], [23], and [18,9] respectively. Note that an even bigger challenge as opposed to usual knowledge discovery setups is the lack of comprehensive benchmarks.…”

Section: Bisociation Discovery Methodsmentioning

confidence: 99%

Towards Bisociative Knowledge Discovery

Berthold

2012

Bisociative Knowledge Discovery

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Knowledge discovery generally focuses on finding patterns within a reasonably well connected domain of interest. In this article we outline a framework for the discovery of new connections between domains (so called bisociations), supporting the creative discovery process in a more powerful way. We motivate this approach, show the difference to classical data analysis and conclude by describing a number of different types of domain-crossing connections. Extended version of [1].

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Section: Bisociation Discovery Methodsmentioning

confidence: 99%

Towards Bisociative Knowledge Discovery

Berthold

2012

Bisociative Knowledge Discovery

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“…Figure 2 shows a screenshot of the interface visualizing a music collection. 6 Each track is displayed as a star, i.e., a point, with its brightness and-to some extend-its hue depending on a predefined importance measure (here a play count obtained from last.fm -other measures such as a general popularity or ratings are possible). A spatially well distributed subset of the collection (specified by filters) is additionally displayed as an album cover for orientation.…”

Section: The Musicgalaxy Visualizationmentioning

confidence: 99%

Bisociative Music Discovery and Recommendation

Stober

Haun

Nürnberger

2012

Bisociative Knowledge Discovery

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Abstract. Surprising a user with unexpected and fortunate recommendations is a key challenge for recommender systems. Motivated by the concept of bisociations, we propose ways to create an environment where such serendipitous recommendations become more likely. As application domain we focus on music recommendation using MusicGalaxy, an adaptive user-interface for exploring music collections. It leverages a nonlinear multi-focus distortion technique that adaptively highlights related music tracks in a projection-based collection visualization depending on the current region of interest. While originally developed to alleviate the impact of inevitable projection errors, it can also adapt according to user-preferences. We discuss how using this technique beyond its original purpose can create distortions of the visualization that facilitate bisociative music discovery.

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“…This part starts with a more general discussion of methods for interactive data exploration. Therefore, the chapter of Gossen et al [1] provides first a brief review of methods and tools for the interactive exploration of graphs with a focus on approaches to support bisociative discoveries. Furthermore, a critical discussion of the challenges of evaluating the performance and quality of exploration tools is given.…”

Section: Contributionsmentioning

confidence: 99%