Incremental Clustering of Mobile Objects

Sigal Elnekave, Mark Last, Oded Maimon
2007 2007 IEEE 23rd International Conference on Data Engineering Workshop  
Moving objects are becoming increasingly attractive to the data mining community due to continuous advances in technologies like GPS, mobile computers, and wireless communication devices. Mining spatio-temporal data can benefit many different functions: marketing team managers for identifying the right customers at the right time, cellular companies for optimizing the resources allocation, web site administrators for data allocation matters, animal migration researchers for understanding
more » ... on patterns, and meteorology experts for weather forecasting. In this research we use a compact representation of a mobile trajectory and define a new similarity measure between trajectories. We also propose an incremental clustering algorithm for finding evolving groups of similar mobile objects in spatio-temporal data. The algorithm is evaluated empirically by the quality of object clusters (using Dunn and Rand indexes), memory space efficiency, execution times, and scalability (run time vs. number of objects).
doi:10.1109/icdew.2007.4401044 dblp:conf/icde/ElnekaveLM07 fatcat:uv2pesdkdfbqpdfokemkvpmc5i