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I am looking for the best method to go from a sequence of events such as

time event
1 a
2 b
3 a
4 b
5 c
6 d
7 c
8 d
9 e

Where each letter corresponds to a certain event that occurs at a time. I want to reduce the number of events by aggregating frequently occurring events into a new event. A possible solution data set would look like,

1 a'
2 a'
3 a'
4 a' 
5 b'
6 b'
7 b'
8 b'
9 e

where the clusters are created because they occur in a sequence following each other.

I was looking at the text mining algorithms in R with tm or RNA sequenceing with edgeR. But I have no experience in this so I was hoping that someone can shed me some light on a common approach for this type of problems.

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  • 1
    $\begingroup$ Lots of details and context is missing. It is unclear what those sequences are and what does it mean to transform. A wild wild guess: you have a set of sequences with corresponding output sequences. RNN can learn such transoframation (similar to machine translation, e.g. from english to french). $\endgroup$ – Vladislavs Dovgalecs Oct 28 '15 at 20:47
  • $\begingroup$ Are you wanting to aggregate events by some common features of their "type" (independent of where they appear in the sequence) e.g. you consider a' in your example to cover a and b because they are similar in some way? Or are you wanting to cluster events by when they occur in the sequence e.g. you are combining a and b because they occur together? $\endgroup$ – Neil Slater Oct 29 '15 at 10:09
  • $\begingroup$ They occur in sequence. Not because they have any kind of resemblance. $\endgroup$ – Stereo Oct 29 '15 at 12:58
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You may use ngrams to get the frequent sequences of you events.

Here a little example

 library(tau)
 seq <- "ababcdcde"
 textcnt(seq, method="ngram", n=3L, decreasing=TRUE)

   _   a  ab   b   c  cd   d  _a _ab aba abc  ba bab  bc bcd cdc cde  dc dcd  de de_   e  e_ 
   2   2   2   2   2   2   2   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1

Select the longest and most frequent ngram, here ab and cd. You may also trade off the length and frequency to get maximal compression. The n parameter limits the length of the ngram.

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