Real time project !!!!"Sequential Patterns Efficient Mining"

In this paper, we propose to measure pattern frequentness based on the possible
world semantics. We establish two uncertain sequence data models abstracted
from many real-life applications involving uncertain sequence data, and formulate
the problem of mining probabilistically frequent sequential patterns (or
p-FSPs) from data that conform to our models. However, the number of possible
worlds is extremely large, which makes the mining prohibitively expensive.
Inspired by the famous PrefixSpan algorithm, we develop two new algorithms,
collectively called U-PrefixSpan, for p-FSP mining. U-PrefixSpan effectively
avoids the problem of “possible worlds explosion”, and when combined with our
four pruning and validating methods, achieves even better performance.www.fu-vision .com
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