Results 71 to 80 of about 2,690 (229)
New Improved Algorithm for Mining Privacy - Preserving Frequent Itemsets
, 2020 Due to the increasing use of very large databases and data warehouses, mining useful information and helpful knowledge from transactions is evolving into an important research area.
Awasthy, Rashmi
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Mining frequent itemsets from streaming transaction data using genetic algorithms
Journal of Big Data, 2020 This paper presents a study of mining frequent itemsets from streaming data in the presence of concept drift. Streaming data, being volatile in nature, is particularly challenging to mine.
Sikha Bagui, Patrick Stanley
doaj +1 more source
Advances in Civil Engineering, Volume 2025, Issue 1, 2025.The coal industry has always been a typically high‐risk industry with frequent accidents and extremely adverse impacts. Cases of accidents in coal mine ventilation systems serve as a concentrated demonstration of accident hazards and hold significant value for identifying key risk factors that may induce disasters in coal mine ventilation systems. This
Mingjia Jing +4 more
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MINING FREQUENT itemsets using advanced partition APPROACH [PDF]
, 2009 Frequent itemsets mining plays an important part in many data mining tasks. This technique has been used in numerous practical applications, including market basket analysis. This paper presents mining frequent itemsets in large database of medical sales
Khin Myat Myat Moe +3 more
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A False Negative Maximal Frequent Itemsets Mining Algorithm over Stream
, 2011 Maximal frequent itemsets are one of several condensed representations of frequent itemsets, which store most of the information contained in frequent itemsets using less space, thus being more suitable for stream mining.
Ning Zhang, Hai Feng Li
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AN EFFICIENT ALGORITHM FOR MINING HIGH UTILITY ITEMSETS
Tạp chí Khoa họcHigh utility itemsets (HUIs) mining is the finding of itemsets that satisfy a user-defined minimum utility threshold. Many successful studies in this field have been carried out, however they are all reliant on Tidset techniques, which records the ...
Nguyen Thi Thanh Thuy*, Nguyen Van Le, Manh Thien Ly
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Mining frequent closed itemsets with the frequent pattern list [PDF]
Proceedings 2001 IEEE International Conference on Data Mining, 2002 The mining of a complete set of frequent itemsets will lead to a huge number of itemsets. Fortunately, this problem can be reduced to the mining of frequent closed itemsets (FCIs), which results in a much smaller number of itemsets. The approaches to mining frequent closed itemsets can be categorized into two groups: those with candidate generation and
Tseng, Fan-Chen +2 more
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Unveiling Success Drivers in Gaming: A Machine Learning Study Across Steam, Twitch, and Metacritic
International Journal of Computer Games Technology, Volume 2025, Issue 1, 2025.This study employs machine learning to assess the relative impact of major platforms—Steam, Twitch, and Metacritic—on video game revenue. Through an integrated analysis of three comprehensive datasets comprising commercially successful titles on Steam, key predictors of financial performance were identified.
Jiesi Ma, Michael J. Katchabaw
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Data‐Driven Materials Research and Development for Functional Coatings
Advanced Science, Volume 11, Issue 42, November 13, 2024.Functional coatings play a vital role in various industries for their protective and functional properties. However, its design often involves time‐consuming experimentation with multiple materials and processing parameters. To overcome these limitations, data‐driven approaches are gaining traction in materials science. This review provides an overview
Kai Xu +8 more
wiley +1 more source
An improvement of FP-Growth association rule mining algorithm based on adjacency table
MATEC Web of Conferences, 2018 FP-Growth algorithm is an association rule mining algorithm based on frequent pattern tree (FP-Tree), which doesn’t need to generate a large number of candidate sets.
Yin Ming +3 more
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