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We present a new method for detecting descriptive community patterns capturing exceptional (sequential) link trails. For that, we provide a novel problem formalization: We model sequential data as first-order Markov chain models, mapped to an attributed weighted network represented as a graph. Then, we detect subgraphs (communities) using exceptional model mining techniques: We target subsets of sequential...
A central challenge in education is to match instruction to the characteristics and learning styles of students in order to optimize learning. In this article, we intend to outline our approach to supporting personalized learning strategies by constructing dynamical student profiling using ubiquitous computing capability. This profiling includes recorded data on students' affective responses to learning...
This paper investigates how data mining can be applied in functional debug, which is formulated as the problem of explaining a functional simulation error based on human-understandable machine states. We present a rule discovery methodology comprising two steps. The first step selects relevant state variables for constructing the mining dataset. The second step applies rule learning to extract rules...
The term ‘big data analytics’ emerged in order to engage in the ever increasing amount of scientific and engineering data with general analytics techniques that support the often more domain-specific data analysis process. It is recognized that the big data challenge can only be adequately addressed when knowledge of various different fields such as data mining, machine learning algorithms, parallel...
The multi-agent simulation consists in using a set of interacting agents to reproduce the dynamics and the evolution of the phenomena that we seek to simulate. It is considered now as an alternative to classical simulations based on analytical models. But, its implementation remains difficult, particularly in terms of behaviors extraction and agents modelling. This task is usually performed by the...
The recent advance in SNP genotyping has made a significant contribution to reduction of the costs for large-scale genotyping. The development also has dramatically increased the size of the SNP genotype data. The increase of the volume of the data, however, has posed a huge obstacle to the conventional analysis techniques that are typically vulnerable to the high-dimensionality problem. To address...
Web log files store data related to the use of a website. Analyzing these data in detail is therefore crucial for improving the user browsing experience. However, usually Web log data are stored in flat files in different formats which hinders their analysis, thus obliging to use specific Web log analysis tools. In this context, approaches for structuring Web log data to better analyze them are highly...
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