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On Graph Kernels: Hardness Results and Efficient Alternatives
As most ‘real-world’ data is structured, research in kernel methods has begun investigating kernels for various kinds of structured data, but only very specific graphs such as trees and strings have been considered. Expand
An Algorithm for Multi-relational Discovery of Subgroups
An algorithm is described for finding statistically unusual subgroups in a multi-relation database that uses optimistic estimate and minimal support pruning, an optimal refinement operator and sampling to ensure efficiency and can easily be parallelized. Expand
Geovisual analytics for spatial decision support: Setting the research agenda
This article summarizes the results of the workshop on Visualization, Analytics & Spatial Decision Support, which took place at the GIScience conference in September 2006, and suggests a new research direction ‘Geovisual Analytics for Spatial decision Support’, which emphasizes the importance of visualization and interactive visual interfaces and the link with the emerging research discipline of Visual Analytics. Expand
Visual analytics tools for analysis of movement data
It is argued that by using the right visual analytics tools for the analysis of massive collections of movement data, it is possible to effectively support human analysts in understanding movement behaviors and mobility patterns. Expand
Active Hidden Markov Models for Information Extraction
This paper considers the more challenging task of learning hidden Markov models (HMMs) when only partially (sparsely) labeled documents are available for training, and describes an EM style algorithm for learning HMMs from partially labeled data. Expand
Cyclic pattern kernels for predictive graph mining
The experimental results show that cyclic pattern kernels can be computed quickly and offer predictive performance superior to recent graph kernels based on frequent patterns. Expand
Proceedings of the 22nd international conference on Machine learning
This volume, which is also available online from http://www.machinelearning.org, contains the papers accepted for presentation at ICML-2005, the 22nd lnternational Conference on Machine Learning,Expand
A conceptual framework and taxonomy of techniques for analyzing movement
A conceptual framework is presented that describes in a systematic and comprehensive way the possible types of information that can be extracted from movement data and on this basis defines the respective types of analytical tasks. Expand
Tight Optimistic Estimates for Fast Subgroup Discovery
This paper shows that optimistic estimate pruning can be developed into a sound and highly effective pruning approach for subgroup discovery and presents tight optimistic estimates for the most popular binary and multi-class quality functions, and presents a family of increasingly efficient approximations to these optimal functions. Expand
Visual Analytics of Movement
The authors demonstrate that visual analytics of movement data can yield exciting insights into the behavior of moving persons and objects, but can also lead to an understanding of the events that transpire when things move. Expand