Finding Local and Periodic Association Rules from Fuzzy Temporal Data

The problem of finding association rules from a dataset is to find all possible associations that hold among the items, given a minimum support value and a minimum confidence. This involves finding frequent sets first and then the association rules that hold within the items in the frequent sets. The problem of mining temporal association rules from temporal dataset is to find association rules between items that hold within certain time intervals but not throughout the dataset. This involves finding frequent sets that are frequent at certain time intervals and then association rules among the items present in the frequent sets. In some of the applications the time of transaction is imprecise; we call the associated dataset as fuzzy temporal dataset. In such datasets, we may find set of items that are frequent in certain fuzzy time intervals. We call these as locally frequent sets over fuzzy time intervals and the associated association rules as local association rule over fuzzy time intervals. These association rules cannot be discovered in the usual way because of fuzziness involved in temporal features. Normally these association rules are periodic in nature. We call such rules as periodic association rules over fuzzy time interval. We propose modification to the A-priori algorithm to compute locally frequent sets and to extract periodic frequent sets and periodic association rules from fuzzy temporal data. Kew-words: Core of a fuzzy number, Data mining, frequent sets, fuzzy membership function, α-cut.