A simple new algorithm is suggested for frequent itemset mining, using item probabilities as the basis for generating candidates. The method first finds all the frequent items, an...
In recent years, mining frequent itemsets over uncertain data has attracted much attention in the data mining community. Unlike the corresponding problem in deterministic data, th...
Correlated or discriminative pattern mining is concerned with finding the highest scoring patterns w.r.t. a correlation measure (such as information gain). By reinterpreting corre...
This paper explores the generation of candidates, which is an important step in frequent itemset mining algorithms, from a theoretical point of view. Important notions in our prob...
This work proposes a theoretical guideline in the specific area of Frequent Itemset Mining (FIM). It supports the hypothesis that the use of neural network technology for the prob...