Web Mining and Social Networking: Techniques and ... - tud.ttu.ee

38 3 Algorithms and TechniquesCustomer ID Transaction Time Items Bought100 July 3 '07 apple100 July 6 '07 strawberry100 July 8 '07 banana, strawberry100 July 10 '07 pear100 July 12 '07 apple, banana, strawberry100 July 16 '07 apple100 July 21 '07 pear200 July 4 '07 banana200 July 7 '07 strawberry, pear200 July 9 '07 apple200 July 10 '07 strawberry200 July 15 '07 banana, pear300 July 13 '07 pear300 July 15 '07 banana, strawberry300 July 21 '07 apple, strawberry300 July 24 '07 strawberry, pearFig. 3.3. Database Sorted by Customer ID and Transaction TimeLarge ItemsetsapplebananastrawberrypearMapped ToabcdFig. 3.4. Large ItemsetsCustomer ID Customer Sequence100 < ac(bc)d(abc)ad >200 < b(cd)ac(bd) >300 < d(bc)(ac)(cd) >Fig. 3.5. Transformed Database• Transformation Phase: All the large itemsets are mapped into a series of integers andthe original database is converted by replacing the itemsets. For example, with the help ofthe mapping table in Figure 3.4, the transformed database is obtained, as shown in Figure3.5.• Sequence Phase: Mine the transformed database and find all frequent sequential patterns.• Maximal Phase: Prune those patterns which are contained in other sequential patterns. Inother words, only maximum sequential patterns are remained.Since most of the phases are straightforward, researches focused on the sequence phase.AprioriAll [11] was first proposed based on the Apriori algorithm in association rule mining[9]. There are two steps to mine sequential patterns, i.e., candidate generation and test.The candidate generation process is similar to the AprioriGen in [9]. The Apriori propertyis applied to prune those candidate sequences whose subsequence is not frequent. The differenceis that when the authors generate the candidate by joining the frequent patterns in the