OPTICS: ordering points to identify the clustering structure
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Adaptive event detection with time-varying poisson processes
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Visual analytics tools for analysis of movement data
ACM SIGKDD Explorations Newsletter - Special issue on visual analytics
Nonlinear Dimensionality Reduction
Nonlinear Dimensionality Reduction
Visually driven analysis of movement data by progressive clustering
Information Visualization
Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems
Clustering vessel trajectories with alignment kernels under trajectory compression
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part I
Understanding of Internal Clustering Validation Measures
ICDM '10 Proceedings of the 2010 IEEE International Conference on Data Mining
Perception beyond the Here and Now
Computer
| Hi-index | 0.00 |
Traffic and mobility mining are fascinating and fast growing areas of data mining and geographical information systems that impact the lives of billions of people every day. Another well-known scientific field that impacts lives of billions is biological sequence analysis. It has experienced an incredible evolution in the recent decade, especially since the Human Genome project. Although, a very first link between both fields has been established already in the early 90ies, many recent papers on mobility mining seem to be unaware of it. We therefore revisit the link and show that many unexplored and novel mobility mining methods fall naturally out of it. Specifically, using advanced discretization techniques for stay-point detection and map matching, we turn traffic sequences into a "biological" ones. Then, we introduce a novel distance function that enables us to directly apply the rich toolbox for biological sequence analysis to it. For instance, by just looking at complex traffic data through the biological glasses of sequence logos we get a novel, easy-to-grasp visualization of data, called "Traffic Logos". For clustering and prediction tasks, our empirical evaluation on three real-world data sets demonstrates that revisiting the link can yield performance as good as state-of-the-art data mining techniques.