A Summary of Research on Frequent Itemsets Mining Technology
نویسندگان
چکیده
منابع مشابه
Multi-Sorted Inverse Frequent Itemsets Mining: On-Going Research
Inverse frequent itemset mining (IFM) consists of generating artificial transactional databases reflecting patterns of real ones, in particular, satisfying given frequency constraints on the itemsets. An extension of IFM called manysorted IFM, is introduced where the schemes for the datasets to be generated are those typical of Big Tables, as required in emerging big data applications, e.g., so...
متن کاملMining Frequent Itemsets A Perspective from Operations Research
Many papers on frequent itemsets have been published. Besides some contests in this field were held. In the majority of the papers the focus is on speed. Ad hoc algorithms and datastructures were introduced. In this paper we put most of the algorithms in one framework, using classical Operations Research paradigms such as backtracking, depthfirst and breadth-first search, and branch-and-bound. ...
متن کاملOn Differentially Private Frequent Itemsets Mining
Frequent itemsets mining finds sets of items that frequently appear together in a database. However, publishing this information might have privacy implications. Accordingly, in this paper we are considering the problem of guaranteeing differential privacy for frequent itemsets mining. We measure the utility of a frequent itemsets mining algorithm by its likelihood to produce a complete and sou...
متن کاملOn Mining Max Frequent Generalized Itemsets
A fundamental task of data mining is to mine frequent itemsets. Since the number of frequent itemsets may be large, a compact representation, namely the max frequent itemsets, has been introduced. On the other hand, the concept of generalized itemsets was proposed. Here, the items form a taxonomy. Although the transactional database only contains items in the leaf level of the taxonomy, a gener...
متن کاملEfficiently Mining Maximal Frequent Itemsets
We present GenMax, a backtrack search based algorithm for mining maximal frequent itemsets. GenMax uses a number of optimizations to prune the search space. It uses a novel technique called progressive focusing to perform maximality checking, and diffset propagation to perform fast frequency computation. Systematic experimental comparison with previous work indicates that different methods have...
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ژورنال
عنوان ژورنال: Procedia Computer Science
سال: 2018
ISSN: 1877-0509
DOI: 10.1016/j.procs.2018.04.276