| Summary: | 碩士 === 國立中山大學 === 資訊工程學系研究所 === 107 === In data mining, frequent-itemset mining and erasable-itemset mining are two standard and practical techniques for finding useful itemsets. Frequent-itemset mining is a significant pre-processing step in the search for association rules and is mainly conducted based on the frequencies of the itemsets in a transaction database. On the other hand, erasable-itemset mining is often applied to product production planning, and it identifies the itemsets that would not have a significant impact on production profit if removed. Although the two mining techniques seem to be independent, we show that they are actually related to each other in this thesis. We formally prove that these two mining techniques possess the property of duality. Besides, we design methods to transform one of the two mining problems into the other and then solve it, and vice versa. The mining results will be identical before and after the transformation. Then, we extend the duality property of the two mining problems to multiple thresholds situation, which defines variant thresholds for different items. Finally, we conduct several experiments to analyze the influence of items’ density on the time consumption of the mining process before and after the transformation.
|
|---|
