Titlebook: Research and Development in Intelligent Systems XXX; Incorporating Applic Max Bramer,Miltos Petridis Conference proceedings 2013 Springer I

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Vertex Unique Labelled Subgraph Miningm, the Right-most Extension VULS Mining (REVULSM) algorithm, which identifies all VULS in a given graph. The performance of REVULSM is evaluated using a real world sheet metal forming application. The experimental results demonstrate that all VULS (Vertex Unique Labelled Subgraphs) can be effectively identified.

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Profiling Spatial Collectivesle for a spatial collective which gives a detailed analysis of its movement patterns; such profiles could then be used to identify the type of spatial collective. A computer program has been developed that allows the method to be applied to a spatiotemporal dataset.

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Classification Based on Homogeneous Logical Proportionson an ongoing work and contributes to a comparative study of the logical proportions predictive accuracy on a set of standard benchmarks coming from UCI repository. Logical proportions constitute an interesting framework to deal with binary and/or nominal classification tasks without introducing any metrics or numerical weights.

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Predicting Occupant Locations Using Association Rule Miningd on historical occupant movements and any available real time information, or based on recent occupant movements. We show how association rule mining can be adapted for occupant prediction and evaluate both approaches against existing approaches on two sets of real occupants.

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Pattern Graphs: Combining Multivariate Time Series and Labelled Interval Sequences for Classificatioons exist to deal with labelled intervals. Finding the right preprocessing is not only time consuming but also critical for the success of the learning algorithms. In this paper we show how pattern graphs, a powerful pattern language for temporal classification rules, can be extended in order to han