I am working with sequence data which are long lists of malware win-api calls. I am trying to cast the problem of identifying 'malware behavior' into one of finding sequential patterns. I treat each api call as a single item Itemset. The number of different possible items (api calls) is quite large. 

Now, when I apply the SPADE algorithm (see also, Zaki, SPADE: An Efficient Algorithm for Mining Frequent Sequences, Machine Learning, 42, 31–60, 2001) I run into memory problems.
Is there a better alternative way to find sequential patterns among large high vocabulary sequences?


Question: How do C1, C2, C3 relate to C and CSus4 on guitar?
Answer: There are no chords called C1, C2, C3, or C4. In the tab the author just means different versions (voicings) of a C chord; he could as well have called them C_A, C_B, etc. One of those voicings is actually a Cadd9 chord (from low to high):

x 3 2 0 3 3

(the note on the third fret of the b string is a D, i.e. the 9th of C). Note that this chord is different from a C9 chord, the latter being a dominant ninth chord with a minor 7th (a Bb).

There is also no C4 chord, only a Csus4 (suspended fourth), where the third (the E) is replaced by the 4th (the F).

Ed Sheeran actually uses only two types of C chords in that song. The 'standard' open string C chord:

x 3 2 0 1 0

and the Cadd9 chord shown above.