Results 151 to 160 of about 2,316 (204)

Efficient Incremental High Utility Itemset Mining

Proceedings of the ASE BigData & SocialInformatics 2015, 2015
High-utility itemset mining (HUIM) in transaction databases is an important data mining task with wide applications. However, most HUIM algorithms assume the unrealistic assumption that databases are static. To address this issue, algorithms have been designed to maintain high-utility itemsets in dynamic databases. However, these incremental algorithms
Philippe Fournier-Viger, Jerry Chun-Wei Lin, Ted Gueniche, Prashant Barhate   +3 more
openaire   +1 more source

Mining High Transaction-Weighted Utility Itemsets

2010 Second International Conference on Computer Engineering and Applications, 2010
In this paper, we design a new kind of patterns, named high transaction-weighted utility itemsets, which considers not only individual profits and quantities of the items in a transaction, but also the contribution of each transaction in a database. We also propose a two-phased mining algorithm to discover high transaction-weighted utility itemsets ...
Guo-Cheng Lan, Tzung-Pei Hong, Vincent S. Tseng   +2 more
openaire   +1 more source

Mining Local High Utility Itemsets

2018
High Utility Itemset Mining (HUIM) is the task of analyzing customer transactions to find the sets of items that yield a high utility (e.g. profit). A major limitation of traditional HUIM algorithms is that they do not consider that the utility of itemsets vary over time.
Philippe Fournier-Viger, Yimin Zhang, Jerry Chun-Wei Lin, Hamido Fujita, Yun Sing Koh   +4 more
openaire   +1 more source

Correlated High Average-Utility Itemset Mining

2020
High average-utility itemset (HAUI) mining is an advancement over high utility itemset mining, where average-utility is used instead of utility measure to discover meaningful patterns. It has been discussed in several past studies that significance of utility-based patterns can be amplified if items in the patterns are correlated.
Krishan Kumar Sethi, Dharavath Ramesh
openaire   +1 more source

Mining summarization of high utility itemsets

Knowledge-Based Systems, 2015
Mining interesting itemsets from transaction databases has attracted a lot of research interests for decades. In recent years, high utility itemset (HUI) has emerged as a hot topic in this field. In real applications, the bottleneck of HUI mining is not at the efficiency but at the interpretability, due to the huge number of itemsets generated by the ...
Xiong Zhang, Zhi-Hong Deng
openaire   +1 more source

Mining High-Utility Irregular Itemsets

2019
High-utility itemset mining (HUIM) currently plays an important role in a wide range of applications and data mining community. Several algorithms, methods and data structures have been proposed to improve efficiency of mining for such itemsets. Besides, HUIM is extended in several aspects including the regarding of “regularity or irregularity of ...
Supachai Laoviboon, Komate Amphawan
openaire   +1 more source

Discovering high utility itemset using MapReduce

2016 3rd International Conference on Systems and Informatics (ICSAI), 2016
Based on the MapReduce framework, we propose HUIMR algorithm on discovering high utility itemset (HUI). The HUIMR algorithm consists of counting and mining two stages. For the counting stage, MapReduce is used to calculate high transaction-weighted utilization items.
Wei Song, Jiapei Xu
openaire   +1 more source

Mining Cross-Level High Utility Itemsets

2020
Many algorithms have been proposed to find high utility itemsets (sets of items that yield a high profit) in customer transactions. Though, it is useful to analyze customer behavior, it ignores information about item categories. To consider a product taxonomy and find high utility itemsets describing relationships between items and categories, the ML ...
Philippe Fournier-Viger, Ying Wang, Jerry Chun-Wei Lin, Jose Maria Luna, Sebastian Ventura   +4 more
openaire   +1 more source

Towards Efficient Discovery of Target High Utility Itemsets

2022 IEEE International Conference on Data Mining Workshops (ICDMW), 2022
Finding High Utility Itemsets (HUls) in databases is crucial for identifying items that are of high importance (like profit) for decision-making. However, current High Utility Itemset Mining (HUIM) algorithms often ignore the interest or target of users in favor of effectively identifying categories of HUls using various measures and constraints.
Vincent Mwintieru Nofong, Priscilla Owiredu Okai, Hamidu Abdel-Fatao, Selasi Kwashie, Michael Bewong, John Wondoh   +5 more
openaire   +2 more sources

Efficient closed high-utility itemset mining

Proceedings of the 31st Annual ACM Symposium on Applied Computing, 2016
This paper presents a novel algorithm for discovering closed high-utility itemsets (CHUIs) efficiently. It proposes three strategies to mine CHUIs efficiently: closure jumping, forward closure checking and backward closure checking. It also relies on two new upper-bounds named local utility and sub-tree utility to prune the search space, and a Fast ...
Philippe Fournier-Viger, Souleymane Zida, Jerry Chun-Wei Lin, Cheng-Wei Wu, Vincent S. Tseng   +4 more
openaire   +1 more source