The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
Mounir Ben Ayed - One of the best experts on this subject based on the ideXlab platform.
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Enhanced visual Data Mining Process for dynamic decision-making
Knowledge Based Systems, 2016Co-Authors: Hela Ltifi, Emna Benmohamed, Christophe Kolski, Mounir Ben AyedAbstract:Data Mining has great potential in extracting useful knowledge from large amount of temporal Data for dynamic decision-making. Moreover, integrating visualization in Data Mining, known as visual Data Mining, allows combining the human ability of exploration with the analytical Processing capacity of computers for effective problem solving. To design and develop visual Data Mining tools, an appropriate Process must be followed. In this context, the goal of this paper is to enhance existing visualization Processes by adapting it under the temporal dimension of Data, the Data Mining tasks and the cognitive control aspects. The proposed Process aims to model the visual Data Mining methods for supporting the dynamic decision-making. We illustrate the steps of our proposed Process by considering the design of the visualization of the temporal association rules technique. This technique was developed to assist physicians to fight against nosocomial infections in the intensive care unit. Actually, an evaluation study in Situ was performed to assess the automatic prediction results as well as the visual representations. At the end, the test of the efficiency of our Process using utility and usability evaluation shows satisfactory.
Maguelonne Teisseire - One of the best experts on this subject based on the ideXlab platform.
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Mining spatio temporal Data
Intelligent Information Systems, 2006Co-Authors: Gennady Andrienko, Donato Malerba, Michael May, Maguelonne TeisseireAbstract:Both the temporal and spatial dimensions add substantial complexity to Data Mining tasks. First of all, the spatial relations, both metric (such as distance) and non-metric (such as topology, direction, shape, etc.) and the temporal relations (such as before and after) are information bearing and therefore need to be considered in the Data Mining methods. Secondly, some spatial and temporal relations are implicitly defined, that is, they are not explicitly encoded in a Database. These relations must be extracted from the Data and there is a trade-off between precomputing them before the actual Mining Process starts (eager approach) and computing them on-the-fly when they are actually needed (lazy approach). Moreover, despite much formalization of space and time relations available in spatio-temporal reasoning, the extraction of spatial/ temporal relations implicitly defined in the Data introduces some degree of fuzziness that may have a large impact on the results of the Data Mining Process. J Intell Inf Syst (2006) 27: 187–190 DOI 10.1007/s10844-006-9949-3
Daniel A Keim - One of the best experts on this subject based on the ideXlab platform.
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Information visualization and visual Data Mining
IEEE Transactions on Visualization and Computer Graphics, 2002Co-Authors: Daniel A KeimAbstract:Never before in history Data has been generated at such high volumes as it is today. Exploring and analyzing the vast volumes of Data becomes increasingly difficult. In- formation visualization and visual Data Mining can help to deal with the flood of information. The advantage of visual Data exploration is that the user is directly involved in the Data Mining Process. There is a large number of information visualization techniques which have been developed over the last decade to support the exploration of large Data sets. In this paper, we propose a classification of information visu- alization and visual Data Mining techniques which is based on the Data type to be visualized, the visualization technique and the interaction and distortion technique. We exemplify the clas- sification using a few examples, most of them referring to techniques and systems presented in this special issue
Hela Ltifi - One of the best experts on this subject based on the ideXlab platform.
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Enhanced visual Data Mining Process for dynamic decision-making
Knowledge Based Systems, 2016Co-Authors: Hela Ltifi, Emna Benmohamed, Christophe Kolski, Mounir Ben AyedAbstract:Data Mining has great potential in extracting useful knowledge from large amount of temporal Data for dynamic decision-making. Moreover, integrating visualization in Data Mining, known as visual Data Mining, allows combining the human ability of exploration with the analytical Processing capacity of computers for effective problem solving. To design and develop visual Data Mining tools, an appropriate Process must be followed. In this context, the goal of this paper is to enhance existing visualization Processes by adapting it under the temporal dimension of Data, the Data Mining tasks and the cognitive control aspects. The proposed Process aims to model the visual Data Mining methods for supporting the dynamic decision-making. We illustrate the steps of our proposed Process by considering the design of the visualization of the temporal association rules technique. This technique was developed to assist physicians to fight against nosocomial infections in the intensive care unit. Actually, an evaluation study in Situ was performed to assess the automatic prediction results as well as the visual representations. At the end, the test of the efficiency of our Process using utility and usability evaluation shows satisfactory.
Gennady Andrienko - One of the best experts on this subject based on the ideXlab platform.
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Mining spatio temporal Data
Intelligent Information Systems, 2006Co-Authors: Gennady Andrienko, Donato Malerba, Michael May, Maguelonne TeisseireAbstract:Both the temporal and spatial dimensions add substantial complexity to Data Mining tasks. First of all, the spatial relations, both metric (such as distance) and non-metric (such as topology, direction, shape, etc.) and the temporal relations (such as before and after) are information bearing and therefore need to be considered in the Data Mining methods. Secondly, some spatial and temporal relations are implicitly defined, that is, they are not explicitly encoded in a Database. These relations must be extracted from the Data and there is a trade-off between precomputing them before the actual Mining Process starts (eager approach) and computing them on-the-fly when they are actually needed (lazy approach). Moreover, despite much formalization of space and time relations available in spatio-temporal reasoning, the extraction of spatial/ temporal relations implicitly defined in the Data introduces some degree of fuzziness that may have a large impact on the results of the Data Mining Process. J Intell Inf Syst (2006) 27: 187–190 DOI 10.1007/s10844-006-9949-3
