2013 | OriginalPaper | Buchkapitel
Stream Mining of Frequent Patterns from Delayed Batches of Uncertain Data
verfasst von : Fan Jiang, Carson Kai-Sang Leung
Erschienen in: Data Warehousing and Knowledge Discovery
Verlag: Springer Berlin Heidelberg
Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
Streams of data can be continuously generated by sensors in various real-life applications such as environment surveillance. Partially due to the inherited limitation of the sensors, data in these streams can be uncertain. To discover useful knowledge in the form of frequent patterns from streams of uncertain data, a few algorithms have been developed. They mostly use the sliding window model for processing and mining data streams. However, for some applications, other stream processing models such as the time-fading model are more appropriate. Moreover, batches of data in the stream may be delayed and not arrived in the intended order. In this paper, we propose mining algorithms that use the time-fading model to mine frequent patterns when these batches in the streams of uncertain data were delayed and arrived out of order.