The dmt4sp prototype is a command line tool to extract episodes and episode rules under the minimal occurrence semantics as defined in 1, supporting various constraints, over a single sequence or several sequences of events.
Contact: Christophe Rigotti
Three kinds of patterns can be extracted¶
serial episode rules having a single event type in the consequent
quantitative episodes: grouping “homogeneous” occurrences of the serial episodes with respect to the time elapsed between the event types (patterns called quantitative episodes and defined in 2)
Support constraints (minimal occurrence semantics)¶
minimum number of occurrences
minimum number of sequences in which the pattern must occur
maximum window size
a kind of maximum gap (not the standard max gap constraint used for sequential patterns)
minimum pattern length
maximum pattern length
last event type of the pattern (for episode rules this allows to set the event type of the consequent, to discard the other rules)
prefix of the pattern (with wildcard place holder)
Some other options¶
a threshold to discard event types that are too frequent
for rules: minimum confidence
for groups of occurrences (quantitative episodes): parameters to define the homogeneity of the groups and their minimum size
several input and output formats (the input can be a single long sequence or a set of sequences)
output of occurrence locations
and more …
Discovery of frequent episodes in event sequences. Mannila, H.,Toivonen, H. and Verkamo, A.I. DMKD Journal, volume 1, pp. 259-289, 1997.
Extracting Trees of Quantitative Serial Episodes. M. Nanni and C. Rigotti. Knowledge Discovery in Inductive Databases 5th International Workshop KDID‘06 Revised Selected and Invited Papers. LNCS 4747, pp. 170-188, 2007.