Comments (5)
Great video -- thank you for sharing!
The short answer is no, not out of the box, since the library consider sequences with singleton items (as opposed to a tuple like (A1, A2, B) being an item).
Some data transformation might be possible during pre-processing to feed the input accordingly, OR, dropping the tuples to check if, in your example, A1, A2, A still shows up as frequent at the end.
That said, under the hood for the MDD propagation, there are great parallels with the PrefixSpan algorithm like the pseudo-projection etc.
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The short answer is no, the software is independent of the definition of an item.
If the item of interest is a tuple as in <A1,A2,B>, then you can transform the data to map them, say to X. Likewise, <A,D> to represent another item Y. Then, the event sequence of tuples [["A1", "A2", "B"], ["A", "D"]] becomes [X, Y].
In essence, what we call "items" does not really matter (it can be single items, or tuples) and is not an off-the-shelf transformation within the library, we leave that definition to the specific application.
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Actually I was interested in investigating the relation within each single tuple element, such as A1 -> D but I think this is not the expected usage.
Thank you for your answer and your work!
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I see, makes sense. If there is a sequence relationship "within" the tuple, may be it is not a static tuple then? As in, items should remain as A1, A2, B and the sequence becomes A1 -> A2 -> B whereby you would have "two" sequences in your first row in the example [["A1", "A2", "B"], ["A", "D"]] --> ["A1", "A2", "B"] as one sequence and ["A", "D"] as another sequence. Just an idea.
Thanks for the positive feedback!
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I guess yes, tuple size is definitely not static.
My question was trying to address whether this algorithm was fitting the use case of PrefixSpan, such as, in this case
["A1", "A2", "B"], ["A", "D"]
evaluating independently single element A1, A2 and B implying A or D and eventually subsets of tuples, e.g. {A1,A2} -> {A}
I attached here this video to explain better the scenario.
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Related Issues (20)
- Memory Leak in C++ HOT 3
- QUESTION HOT 7
- FEATURE REQUEST HOT 1
- 【QUESTION】Any way to limit the minimum length of each pattern? HOT 6
- does it suitable for classification framework? HOT 7
- data still available to download? HOT 6
- Installation error HOT 2
- Integer sequences containing zero (0) as an event HOT 3
- Windows pip installs on 32bit but is missing modules HOT 1
- Patterns with a single event HOT 3
- Feature Request: get attributes associated with mined pattern HOT 10
- Changes to not allow arcs to skip layers HOT 1
- Request for metadata on the event_time column HOT 1
- Performance warning when using pat2feat.get_features HOT 2
- Runtime error: if __name__ == '__main__' HOT 3
- Labels not included in encodings output HOT 3
- Inquiry on performance-improvement in context of small number of long sequences HOT 1
- Failed to use Attributes to add Constraints HOT 2
- Span constraint is imposed by default HOT 3
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