Clustering Uncertain Data Via K-Medoids
SUM '08 Proceedings of the 2nd international conference on Scalable Uncertainty Management
| Hi-index | 0.00 |
The way of collecting sensor data will face a revolution when the newly developing technology of distributed sensor networks becomes fully functional and widely available. Smart sensors will acquire full interconnection capabilities with similar devices, so that run-time data aggregation, parallel computing, and distributed hypothesis formation will become reality with off-the-sheIf components and sensor boards. This revolution started around ten years ago, and now hardware and network are converging on the Jirst convincing solutions. Exploring and exploiting this paradigm are a renovated challenge for the pattern recognition and data mining community. This paper attempts a survey on state-of-the-art of wireless sensor technology, with an eye on data-related problems and technological limits. Although the possibilities seem promising, the today limited computational resources of individual nodes hamper the elaboration of data with recent, computationallyintensive algorithms. New software paradigms must be developed, both creating new techniques or adapting, for network computing, old algorithms of earlier ages of computing.