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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
Research on Frequent Itemset Mining of Imaging Genetics GWAS in Alzheimer's Disease. [PDF]
Genes (Basel), 2022 Liang H +7 more
europepmc +1 more source
weighted frequent itemset algorithm based on dynamic itemset counting
, 2012 基于Apriori的加权频繁项集挖掘算法存在扫描数据集次数多的问题。为此,提出一种基于动态项集计数的加权频繁项集算法。该算法采用权值键树的数据结 构和动态项集计数的方法,满足向下闭合特性,并且动态生成候选频繁项集,从而减少扫描数据集的次数。实验结果证明,该算法生成的加权频繁项集具有较高的效 率和时间性能。The existing weighted frequent itemset mining algorithms which are based on Apriori ...
罗雄飞, 秦丽君
core
Frequent Itemset Mining using QUBO
, 2022 In this paper we propose a R-step approximation to solve frequent itemset mining on quantum hardware like quantum annealing or QAOA. The idea is to search for the set of items where the minimal 2-item frequency is maximal.
Nüßlein, Jonas
core
Dynamic Frequent Itemset Mining Based on Matrix Appriori Algorithm
, 2012 The frequent itemset mining algorithms discover the frequent itemsets from a database. When the database is updated, the frequent itemsets should be updated as well. However, running the frequent itemset mining algorithms with every update is inefficent.
Oğuz, Damla
core +1 more source
Parallel algorithms for mining of frequent itemsets
CoRR, 2021 In the recent decade companies started collecting of large amount of data. Without a proper analyse, the data are usually useless. The field of analysing the data is called data mining. Unfortunately, the amount of data is quite large: the data do not fit into main memory and the processing time can become quite huge.
openaire +2 more sources
Mining Approximate Frequent Itemset from Noisy Data
, 2005 Frequent itemset mining is a popular and important first step in analyzing data sets across a broad range of applications. The traditional, “exact ” approach for finding frequent itemsets requires that every item in the itemset occurs in each supporting ...
Susan Paulsen +4 more
core
Efficient Top-k Frequent Itemset Mining on Massive Data
Data Science and EngineeringTop-k frequent itemset mining (top-k FIM) plays an important role in many practical applications. It reports the k itemsets with the highest supports.
Xiaolong Wan, Xixian Han
doaj +1 more source
Synthesizing Global Exceptional Patterns in Different Data Sources
Journal of Intelligent Systems, 2012 Many large companies transact from multiple branches. It results in generating multiple databases, since local transactions are stored locally. The number of multi-branch companies as well as the number of branches of a multi-branch company is increasing
Adhikari Animesh
doaj +1 more source
Frequent Itemset Mining in Large Datasets a Survey
, 2017 Frequent Itemset Mining is a well-known area in data mining. Most of the techniques available for frequent itemset mining requires complete information about the data which can result in generation of the association rules.
Manish Kumar, Amrit Pal
core +1 more source