| |
| description |
Calendar-based pattern mining aims at identifying patterns on
specific calendar partitions. Potential calendar partitions are for
example: every Monday, every first working day of each month, every
holiday. Providing flexible mining capabilities for calendar-based
partitions is especially challenging in a data stream scenario. The
calendar partitions of interest are not known a priori and at each
point in time only a subset of the detailed data is available. We
show how a data warehouse approach can be applied to this problem.
The data warehouse that keeps track of frequent itemsets holding on
different partitions of the original stream has low storage
requirements. Nevertheless, it allows to derive sets of patterns
that are complete and precise. This work demonstrates the
effectiveness of our approach by a series of experiments.
|
| publisher |
-
|
| type |
Text
|
| Article in Proceedings
|
| source |
In: Proc. of the 9th International Conference on Data Warehousing
and Knowledge Discovery (DaWaK 2007) Regensburg, Germany, 3-7
September, 2007, pp. 438-448
|
| contributor |
IPVS, Anwendersoftware
|
| subject |
Database Applications (CR H.2.8)
|