Results 101 to 110 of about 15,313,493 (129)

A survey of incremental high‐utility itemset mining

WIREs Data Mining and Knowledge Discovery, 2018
Traditional association rule mining has been widely studied. But it is unsuitable for real‐world applications where factors such as unit profits of items and purchase quantities must be considered. High‐utility itemset mining (HUIM) is designed to find highly profitable patterns by considering both the purchase quantities and unit profits of items ...
Wensheng Gan, Jerry Chun-Wei Lin, Philippe Fournier-Viger, Han-Chieh Chao, Tzung-Pei Hong, Hamido Fujita   +5 more
openaire   +2 more sources

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   +2 more sources

Maintaining high-utility itemsets in dynamic databases

2014 International Conference on Machine Learning and Cybernetics, 2014
Utility mining is used to measure the utility values of the purchased items from transactional database. It usually considers not only the occurrence frequencies of items but also the factors of profit, cost and quantity. In the past, many algorithms were proposed to mine high-utility itemsets from a static database.
Chun-Wei Lin 0001, Wensheng Gan, Tzung-Pei Hong, Chien-Ming Chen 0001   +3 more
openaire   +1 more source

Extraction of Top K Itemsets From High Utility Itemsets Using Faster High-Utility Itemset Miner

2018
Frequent itemset mining is the recent research topic in the data mining systems. It generally composes of tremendous volume of frequently searched/retrieved item with low/ high itemset values. This dilemma doesn't satisfy the user's requirements. The utility itemsets is an important topic and it can be measure in terms of weight, value, quantity and ...
Geetha, M., Kavitha, S.
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, José María Luna, Sebastián Ventura   +4 more
openaire   +1 more source

An incremental mining algorithm for high utility itemsets

Expert Systems with Applications, 2012
Association-rule mining, which is based on frequency values of items, is the most common topic in data mining. In real-world applications, customers may, however, buy many copies of products and each product may have different factors, such as profits and prices.
Chun-Wei Lin 0001, Guo-Cheng Lan, Tzung-Pei Hong   +2 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

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   +2 more sources

High-Utility Itemset Mining in Big Dataset

2019 IEEE International Conference on Consumer Electronics - Taiwan (ICCE-TW), 2019
High-utility mining (HUIM) is an extended concept from frequent itemset mining (FIM). It emphasizes the more important factors, such as profits or the weight of an itemset in commercial applications. In this paper, we assume a dataset is too big to be loaded in the memory, then propose a MapReduce framework to handle this kind of situation, and try to ...
Jimmy Ming-Tai Wu, Jerry Chun-Wei Lin, Wei Min   +2 more
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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 0004, Jiapei Xu
openaire   +1 more source