The a. Yes, it is possible to merge <\{1,2\}\{3,4}\> and <{2}{3,4}{5}\> and generate a 5-sequence:<\{1,2\}\{3,4\}\{5\}>. b. No, it is not possible to merge <\{1\}\{2\}\{3\}\{4\}> and <\{2\}\{3,4\}\{5\}\rangle. c. Yes, the candidate <{1}{2}{3}{4}{5}> will survive the pruning step.
It is possible to merge <\{1,2\}\{3,4}\> and <{2}{3,4}{5}\> and generate a 5-sequence:<\{1,2\}\{3,4\}\{5\}>
No, it is not possible to merge <\{1\}\{2\}\{3\}\{4\}> and <\{2\}\{3,4\}\{5\}\rangle and generate a 5-sequence.
This is because the two sequences have different lengths, and the order of events in a sequence matters.
Yes, the candidate <{1}{2}{3}{4}{5}> will survive the pruning step.
This is because it is a supersequence of two frequent 4-sequences, <{1}{2}{3}{4}> and <{2}{3}{4}{5}>.
In sequential pattern mining, a frequent sequence is a sequence of events that occurs in a dataset with a minimum support threshold.
The support of a sequence is the percentage of transactions in the dataset that contain the sequence.
The pruning step is a step in sequential pattern mining that removes candidate sequences that cannot be frequent.
A candidate sequence is a sequence that is generated by merging two frequent sequences.
In order for a candidate sequence to survive the pruning step, it must be a supersequence of two frequent sequences.
A supersequence of a sequence is a sequence that contains all of the events in the original sequence, in the same order.
In the example provided, the candidate sequence <{1}{2}{3}{4}{5}> is a supersequence of the two frequent 4-sequences <{1}{2}{3}{4}> and <{2}{3}{4}{5}>.
Therefore, it will survive the pruning step.