Automatic Item Weight Generation for Pattern Mining and its Application
International Journal of Data Warehousing and Mining
| Hi-index | 0.00 |
Association rule mining is an important model in data mining. Many mining algorithms discover all item associations (or rules) in the data that satisfy the user-specified minimum support and minimum confidence constraints. The weights are associated with the items to solve the question of different importance of the items. But there is another case that the frequency of every item is different from each other. Traditional single support threshold can’t mine association rules effectively. In this paper, the efficient mining of multiple-level association rule is proposed to resolve the above question. This method can not only discover associations that span different hierarchy levels but also have high potential to produce rare but informative item rules. Moreover, an algorithm for mining positive and the negative weighted association rules based on multiple minimum supports is designed simultaneously.