In contrast to association mining, which only discovers relationships between itemsets, we may be interested in exploring patterns shared among transactions where a set of itemsets occur sequentially.
One of the most famous frequent sequential pattern mining algorithms is the Sequential PAttern Discovery using Equivalence Classes (SPADE) algorithm, which employs the characteristics of a vertical database to perform an intersection on an ID list with an efficient lattice search and allows us to place constraints on mined sequences. In this recipe, we will demonstrate how to use cSPADE to mine frequent sequential patterns.
In this recipe, you have to complete the previous recipes by generating transactions with the temporal information and have it stored in the trans
variable.