Multiple average-utility thresholdsHigh Average-Utility Itemset (HAUI) mining is an emerging pattern mining technique to extract meaningful patterns from a transaction dataset. In the past, several HAUI mining algorithms have been developed with efficient upper-bounds and pruning strategies. However, ...
A survey of incremental high-utility itemset mining 作者:Gan, Wensheng; Lin, Jerry Chun-Wei*; Fournier-Viger, Philippe; Chao, Han-Chieh; Hong, Tzung-Pei; Fujita, Hamido 来源:Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery , 2018, 8(2): e1242. DOI:10.1002/widm.1242 ...
The problem of high-utility itemset mining (HUIM) has specifically garnered huge research attention, as it aims to find relevant information on patterns in a database, which conform to a user-defined utility function. The mined patterns are used for making data-backed decisions in the fields...
The utility of an itemset X (in all transactions of a transaction database) is denoted as u(X) and defined as u(X) = Tc∈g(X) u(X, Tc), where g(X) is the set of transactions containing X. The problem of high-utility itemset mining is to discover all high-utility itemsets. ...
Keywords: high-utility itemset, periodic itemset, average periodicity 1 Introduction High-utility itemset mining (HUIM) [4, 5, 8–10, 13] is a popular data mining task. It has attracted a lot of attention in recent years. It extends the traditional problem of Frequent Itemset Mining (...
The term utility refers to the importance or the usefulness of the appearance of the itemset in transactions quantified in terms like profit , sales or any other user preferences. In High Utility Itemset Mining the objective is to identify itemsets that have utility values above a given utility...
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Mining high utility itemsets with time‐aware scheduling using Apache Spark Since the last decade, Market Basket Analysis has been propelled by augmentation of revenue information. Termed as high utility itemset mining (HUIM), this... A Brahmavar,H Venkatarama,G Maiya - 《Concurrency & Computatio...
We next calculate (i) the cumulative mean excess return from day 0–20 = Σt=0,20(Σj=1,N ERjt)/N, which measures the overall response to a set or subset of EHST events, where N = number of firms used to compute the sample average excess return on event day t, and ...
Re- sults show that FCHM is efficient and can prune a huge amount of weakly correlated high-utility itemsets. Keywords: high-utility itemset mining, correlation, bond measure, all- confidence measure, correlated high-utility itemsets 1 Introduction High-Utility Itemset Mining (HUIM) [3, 12,...