| Input: transactional dataset, minimum threshold support, minimum threshold confidence, number of hesitation status. |
| Output: hesitated patterns, hesitated association patterns |
| TD: Initial Transactional Dataset |
| MD: Multilevel Transactional Dataset//after transforming TD into multilevel taxonomy |
| M: highest level in the concept hierarchy//input |
| : store the currently processing level |
| : candidate pattern of size i// |
| : hesitated Patterns of size i// |
| : minimal threshold support as |
| //different for each level in the hierarchy |
| // is the attractiveness support and is the hesitation support of an itemset. |
| = minimal threshold confidence as |
| // is the attractiveness confidence and is the hesitation confidence of an itemset. |
| : hesitation status// |
| Initialize: = 1 |
| Whiledo |
| begin |
| //for each class at each hesitation status |
| Initialize: i = 1 |
| Support_calculation for i-candidate patterns |
| |
| |
| = {candidate patterns} |
| = {hesitated patterns}//after comparing Support with minimum threshold support |
| i = i + 1 |
| Whiledo |
| Begin |
//gen_candidate_patterns from ;//according to hesitation status |
| |
| for all pattern hp1 belongs to do |
| for all pattern hp2 belongs to do |
| if |
| then |
| |
| Prune; |
| for all CP belongs to |
| for all subsets b of CP do |
| if b does not belong to |
| then |
| = {candidate patterns} |
| Calculate the support of each prune candidate patterns at each Hs. |
| |
| Where x and y are the two individual hesitated frequent patterns. |
| |
| |
| = {hesitated patterns} |
| end |
| i = i + 1; |
| end |
| end |
| P = P + 1; |
| end |
| Association Pattern Generation |
| for all item in HP do |
| Construct association |
| Calculate confidence |
| |
| conf |
| if confidence then |
| Output |