Mining Temporal Sequential Patterns Based on Multi-granularities

Mining Sequential Patterns from Temporal Streaming Data

In recent years, emerging applications introduced new constraints for data mining methods. These constraints are typical of a new kind of data: the data streams. In a data stream processing, memory usage is restricted, new elements are generated continuously and have to be considered as fast as possible, no blocking operator can be performed and the data can be examined only once. At this time ...

Sequential Patterns and Temporal Patterns for Text Mining

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Discovering Temporal Patterns in Multiple Granularities

Mining periodic patterns in spatio-temporal sequences at different time granularities

With the advancement of technology, it is now easy to collect the location information of mobile users over time. Spatio-temporal data mining techniques were proposed in the literature for the extraction of patterns from spatio-temporal data. However, current techniques can only extract patterns of the finest time granularity, and therefore overlooks potential patterns available at coarser time...

Mining Frequent Patterns in Data Streams at Multiple Time Granularities

Although frequent-pattern mining has been widely studied and used, it is challenging to extend it to data streams. Compared with mining a static transaction data set, the streaming case has far more information to track and far greater complexity to manage. Infrequent items can become frequent later on and hence cannot be ignored. The storage structure need be dynamically adjusted to reflect th...