Surveying the complementary role of automatic data analysis and visualization in knowledge discovery

Bertini, Enrico; Lalanne, Denis

doi:10.1145/1562849.1562851

Cited by 74 publications

(39 citation statements)

References 20 publications

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“…Similarly, Buja et al [9] review interaction techniques in the general setting of highdimensional data visualization. Hoffman and Grinstein [24] and Bertini and Lalanne [4] discuss visualization methods for high-dimensional data mining, including projection and interaction methods. Keim [33] structures such visualization approaches according to the type of data to be visualized, the actual visualization technique, and the interaction and distortion method.…”

Section: Surveys Of Dr and Interaction Techniquesmentioning

confidence: 99%

Visual Interaction with Dimensionality Reduction: A Structured Literature Analysis

Sacha

Zhang

Sedlmair

et al. 2017

IEEE Trans. Visual. Comput. Graphics

208

139

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Abstract-Dimensionality Reduction (DR) is a core building block in visualizing multidimensional data. For DR techniques to be useful in exploratory data analysis, they need to be adapted to human needs and domain-specific problems, ideally, interactively, and on-the-fly. Many visual analytics systems have already demonstrated the benefits of tightly integrating DR with interactive visualizations. Nevertheless, a general, structured understanding of this integration is missing. To address this, we systematically studied the visual analytics and visualization literature to investigate how analysts interact with automatic DR techniques. The results reveal seven common interaction scenarios that are amenable to interactive control such as specifying algorithmic constraints, selecting relevant features, or choosing among several DR algorithms. We investigate specific implementations of visual analysis systems integrating DR, and analyze ways that other machine learning methods have been combined with DR. Summarizing the results in a "human in the loop" process model provides a general lens for the evaluation of visual interactive DR systems. We apply the proposed model to study and classify several systems previously described in the literature, and to derive future research opportunities.

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Section: Surveys Of Dr and Interaction Techniquesmentioning

confidence: 99%

Visual Interaction with Dimensionality Reduction: A Structured Literature Analysis

Sacha

Zhang

Sedlmair

et al. 2017

IEEE Trans. Visual. Comput. Graphics

208

139

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“…Nonetheless, approaches to integrate DM and IV have been proposed [12]. Following are described approaches to integrate DM and IV, namely: Computer visualization, visually improved data mining.…”

Section: Data Mining Visualizationmentioning

confidence: 99%

Artificial and Natural Intelligence Integration

Alvarado-Pérez

Peluffo-Ordóñez

2015

Distributed Computing and Artificial Intelligence, 12th International Conference

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Abstract. The large amount of data generated by different activities -academic, scientific, business and industrial activities, among others-contains meaningful information that allows developing processes and techniques, which have scientific validity to optimally explore such information. Doing so, we get new knowledge to properly make decisions. Nowadays a new and innovative field is rapidly growing in importance that is Artificial Intelligence, which involves computer processing devices of modern machines and human reasoning. By synergistically combining them -in other words, performing an integration of natural and artificial intelligence-, it is possible to discover knowledge in a more effective way in order to find hidden trends and patterns belonging to the predictive model database. As well, allowing for new observations and considerations from beforehand known data by using data analysis methods as well as the knowledge and skills (of holistic, flexible and parallel type) from human reasoning. This work briefly reviews main basics and recent works on artificial and natural intelligence integration in order to introduce users and researchers on this field integration approaches. As well, key aspects to conceptually compare them are provided.

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“…Visual analytics research [12,22] suggests that computational data analysis methodologies, such as statistics, data mining, or machine learning should be integrated with IVA to create a knowledge discovery framework. The survey by Bertini and Lalanne [5] exemplifies that computational and visual methodologies are complementary. It is promising to aim for solutions where interactive, visual approaches are tightly integrated with automated, computational ones, such that an efficient iterative approach to data analysis becomes possible.…”

Section: Related Workmentioning

confidence: 99%

“…It is promising to aim for solutions where interactive, visual approaches are tightly integrated with automated, computational ones, such that an efficient iterative approach to data analysis becomes possible. Such mixed-initiative knowledge discovery systems take the best of human and machine capabilities [5].…”

Section: Related Workmentioning

confidence: 99%

Interactive visual analysis of families of curves using data aggregation and derivation

Konyha

Lež

Matković

et al. 2012

Proceedings of the 12th International Conference on Knowledge Management and Knowledge Technologies

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Time-series data are regularly collected and analyzed in a wide range of domains. Multiple simulation runs or multiple measurements of the same physical quantity result in ensembles of curves which we call families of curves. The analysis of time-series data is extensively studied in mathematics, statistics, and visualization; but less research is focused on the analysis of families of curves. Interactive visual analysis in combination with a complex data model, which supports families of curves in addition to scalar parameters, represents a premium methodology for such an analysis. In this paper we describe the three levels of complexity of interactive visual analysis we identified during several case studies. The first two levels represent the current state of the art. The newly introduced third level makes extracting deeply hidden implicit information from complex data sets possible by adding data derivation and advanced interaction. We seamlessly integrate data derivation and advanced interaction into the visual exploration to facilitate an in-depth interactive visual analysis of families of curves. We illustrate the proposed approach with typical analysis patterns identified in two case studies from automotive industry.

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Surveying the complementary role of automatic data analysis and visualization in knowledge discovery

Cited by 74 publications

References 20 publications

Visual Interaction with Dimensionality Reduction: A Structured Literature Analysis

Visual Interaction with Dimensionality Reduction: A Structured Literature Analysis

Artificial and Natural Intelligence Integration

Interactive visual analysis of families of curves using data aggregation and derivation

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